Physical adversarial attacks in driving scenarios can expose critical vulnerabilities in visual perception models. However, developing such attacks remains non-trivial due to diverse real-world environmental influences. Existing approaches either struggle to generalize to dynamic environments or fail to achieve consistent physical attack performance. To address these challenges, we propose MAGIC (Mastering Physical Adversarial Generation In Context), a novel framework powered by multi-modal LLM agents to automatically understand the scene context during testing time and generate adversarial patches through synergistic interaction of language and vision understanding. Specifically, MAGIC orchestrates three specialized LLM agents: the adv-patch generation agent masters the creation of deceptive patches via strategic prompt manipulation for text-to-image models; the adv-patch deployment agent ensures contextual coherence by determining optimal deployment strategies based on scene understanding; and the self-examination agent completes this trilogy by providing critical oversight and iterative refinement of both processes. We validate our approach with both digital and physical scenarios, i.e., nuImage and real-world scenes, where both statistical and visual results demonstrate that our MAGIC is powerful and effective for attacking widely applied object detection systems, such as YOLO and DETR series.

Federated recommendations (FRs), facilitating multiple local clients to collectively learn a global model without disclosing user private data, have emerged as a prevalent on-device service. In conventional FRs, a dominant paradigm is to utilize discrete identities to represent clients and items, which are then mapped to domain-specific embeddings to participate in model training. Despite considerable performance, we reveal three inherent limitations that can not be ignored in federated settings, i.e., non-transferability across domains, ineffectiveness in cold-start settings, and potential privacy violations during federated training. To this end, we propose a transferable federated recommendation model, TransFR, which delicately incorporates the general capabilities empowered by pre-trained models and the personalized abilities by fine-tuning local private data. Specifically, it first learns domain-agnostic representations of items by exploiting pre-trained models with public textual corpora. To tailor for FR tasks, we further introduce efficient federated adapter-tuning and post-adaptation personalization, which facilitate personalized adapters for each client by fitting local private data. We theoretically prove the advantages of incorporating adapter tuning in FRs regarding both effectiveness and privacy. Through extensive experiments, we show that our TransFR surpasses state-of-the-art FRs on transferability.

Graph-based retrieval-augmented generation (RAG) enables large language models (LLMs) to incorporate structured knowledge via graph retrieval as contextual input, enhancing more accurate and context-aware reasoning. We observe that for different queries, it could retrieve similar subgraphs as prompts, and thus we propose SubGCache, which aims to reduce inference latency by reusing computation across queries with similar structural prompts (i.e., subgraphs). Specifically, SubGCache clusters queries based on subgraph embeddings, constructs a representative subgraph for each cluster, and pre-computes the key-value (KV) cache of the representative subgraph. For each query with its retrieved subgraph within a cluster, it reuses the pre-computed KV cache of the representative subgraph of the cluster without computing the KV tensors again for saving computation. Extensive experiments on three datasets across multiple LLM backbones and graph-based RAG frameworks demonstrate that SubGCache consistently reduces inference latency with comparable and even improved generation quality, achieving up to 6.68x reduction in time-to-first-token (TTFT).

Tianjun Wei, Huizhong Guo, Yingpeng Du, Zhu Sun, Huang Chen, Dongxia Wang, Jie Zhang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Large Language Models (LLMs) have been widely adopted in ranking systems such as information retrieval (IR) systems and recommender systems (RSs). To alleviate the latency of auto-regressive decoding, some studies explore the single (first) token decoding for ranking approximation, but they suffer from severe degradation in tail positions. Although speculative decoding (SD) methods can be a remedy with verification at different positions, they face challenges in ranking systems due to their left-to-right decoding paradigm. Firstly, ranking systems require strict latency constraints, but verification rounds in SD methods remain agnostic; Secondly, SD methods usually discard listwise ranking knowledge about unaccepted items in previous rounds, hindering future multi-token prediction, especially when candidate tokens are the unaccepted items. In this paper, we propose a Reinforcement Speculative Decoding method for fast ranking inference of LLMs. To meet the ranking systems' latency requirement, we propose an up-to-down decoding paradigm that employs an agent to iteratively modify the ranking sequence under a constrained budget. Specifically, we design a ranking-tailored policy optimization, actively exploring optimal multi-round ranking modification policy verified by LLMs via reinforcement learning (RL). To better approximate the target LLM under the constrained budget, we trigger the agent fully utilizing the listwise ranking knowledge about all items verified by LLMs across different rounds in RL, enhancing the modification policy of the agent. More importantly, we demonstrate the theoretical robustness and advantages of our paradigm and implementation. Experiments on both IR and RS tasks show the effectiveness of our proposed method.

Large language models (LLMs) are increasingly applied to ranking tasks in retrieval and recommendation. Although reasoning prompting can enhance ranking utility, our preliminary exploration reveals that its benefits are inconsistent and come at a substantial computational cost, suggesting that when to reason is as crucial as how to reason. To address this issue, we propose a reasoning routing framework that employs a lightweight, plug-and-play router head to decide whether to use direct inference (Non-Think) or reasoning (Think) for each instance before generation. The router head relies solely on pre-generation signals: i) compact ranking-aware features (e.g., candidate dispersion) and ii) model-aware difficulty signals derived from a diagnostic checklist reflecting the model's estimated need for reasoning. By leveraging these features before generation, the router outputs a controllable token that determines whether to apply the Think mode. Furthermore, the router can adaptively select its operating policy along the validation Pareto frontier at deployment time, enabling dynamic allocation of computational resources toward instances most likely to benefit from Think under varying system constraints. Experiments on three public ranking datasets with different scales of open-source LLMs show consistent improvements in ranking utility with reduced token consumption (e.g., +6.3% NDCG@10 with –49.5% tokens on MovieLens with Qwen3-4B), demonstrating reasoning routing as a practical solution to the accuracy-efficiency trade-off.

Large language models (LLMs) have shown promising potential for next Point-of-Interest (POI) recommendation. However, existing methods only perform direct zero-shot prompting, leading to ineffective extraction of user preferences, insufficient injection of collaborative signals, and a lack of user privacy protection. As such, we propose a novel Multitask Reflective Large Language Model for Privacy-preserving Next POI Recommendation (MRP-LLM), aiming to exploit LLMs for better next POI recommendation while preserving user privacy. Specifically, the Multitask Reflective Preference Extraction Module first utilizes LLMs to distill each user's fine-grained (i.e., categorical, temporal, and spatial) preferences into a knowledge base (KB). The Neighbor Preference Retrieval Module retrieves and summarizes the preferences of similar users from the KB to obtain collaborative signals. Subsequently, aggregating the user's preferences with those of similar users, the Multitask Next POI Recommendation Module generates the next POI recommendations via multitask prompting. Meanwhile, during data collection, a Privacy Transmission Module is specifically devised to preserve sensitive POI data. Extensive experiments on three real-world datasets demonstrate the efficacy of our proposed MRP-LLM in providing more accurate next POI recommendations with user privacy preserved.

Large Language Models (LLMs) have been widely adopted in ranking systems, specifically for reranking tasks. Despite the effectiveness, the auto-regressive decoding of LLMs leads to inference latency due to the memory-bandwidth-bound. To alleviate this bottleneck, prior studies have explored single token decoding as an approximation, but they suffer from performance degradation at the tail positions of ranking. In this paper, we propose a Single Forward Pass (SFP)-based method to pre-verify multiple rankings using tree attention, approximating auto-regressive decoding by relevant sub-rankings at each step. However, verifying all possible ranking permutations will lead to factorial-level token computation (N!), making it intractable within SFP. To this end, we first reduce item ranking permutations to combinations (2N), based on the empirical observation that the LLM's next-item generation is less sensitive to the exact ordering of preceding items. Furthermore, we divide the full ranking into K sub-rankings and aggregate their individual probabilities, which further decreases the verification space to 2⌈N/K⌉ • K << 2N. However, naïvely aggregating sub-ranking probabilities leads to inaccurate estimates in listwise ranking. To overcome this, we introduce a Möbius inversion model that explicitly decompose the individual contribution of subsets within a complete lattice, as verified by tree attention. Then, we learn their higher-order effects with a hierarchical self-attention model to reconstruct the full ranking probability. Experiments on both information retrieval and recommendation tasks show the effectiveness of our proposed method.

Expert-sharing patterns have emerged as promising paradigms for multi-task learning (MTL) in recommender systems, enabling efficient resource allocation and dynamic modeling of diverse tasks. In this paper, we observe that high-gating (leader) and low-gating (auxiliary) experts play distinct roles in MTL: leader experts dominate task-specific predictions, while auxiliary experts, despite their lower gating scores, often contain complementary knowledge that can enhance model performance. However, critical challenges persist: how to effectively identify and utilize the knowledge of the leader and auxiliary experts in a synergistic manner? To address this, we propose a novel Dynamic Experts Synergy (DES) mechanism that integrates Entropy-driven Experts Classification (EEC) and Multi-view Knowledge Recycle (MVKR). EEC dynamically partitions experts into leader and auxiliary groups by analyzing task-specific prediction and gating entropy, enabling adaptive allocation aligned with real-time task difficulty. MVKR effectively revisits knowledge from auxiliary experts through utility, diversity, and task-relatedness perspectives, ensuring comprehensive knowledge utilization. Extensive experiments on five datasets demonstrate the superiority of our DES against state-of-the-art methods.

Battery health prognostics are critical for ensuring safety, efficiency and sustainability in modern energy systems. However, it has been challenging to achieve accurate and robust prognostics due to complex battery degradation behaviors with nonlinearity, noises, capacity regeneration, etc. Existing data-driven models capture temporal degradation features but often lack knowledge guidance, which leads to unreliable long-term health prognostics. To overcome these limitations, we propose KARMA, a knowledge-aware model with frequency-adaptive learning for battery capacity estimation and remaining useful life prediction. The model first performs signal decomposition to derive battery signals in different frequency bands. A dual-stream deep learning architecture is developed, where one stream captures long-term low-frequency degradation trends and the other models high-frequency short-term dynamics. KARMA regulates the prognostics with knowledge, where battery degradation is modeled as a double exponential function based on empirical studies. Our dual-stream model is used to optimize the parameters of the knowledge with particle filters to ensure physically consistent and reliable prognostics and uncertainty quantification. Experimental study demonstrates KARMA’s superior performance, achieving average error reductions of 50.6% and 33.3% over state-of-the-art algorithms for battery health prediction on two mainstream datasets, respectively. These results highlight KARMA’s robustness, generalizability and potential for safer and reliable battery management across diverse applications.

Backdoor attacks on reinforcement learning implant a backdoor in a victim's policy. Once the victim observes trigger signals, it switches to the abnormal mode and fails its task. Most existing attacks assume the adversary can arbitrarily modify the victim's observations (i.e., vision-perturbation), which may be impractical. A more practical way is letting one adversary agent use its actions to affect the victim's observations (i.e., action-perturbation). However, in partially observable multiagent systems, agents may not always be able to observe others. When and how much the adversary agent can affect others' observations are uncertain. Also, we want the adversary agent to trigger others' backdoors with a few actions for stealthiness. To achieve that, we first design a novel training framework to produce auxiliary rewards that measure the influence of each trigger action on others' observations. We then use these rewards to train a trigger policy that guides the adversary agent to efficiently affect others' observations. Given the affected observations, we train the other agents to perform abnormally. Experiments show that the proposed method enables the adversary agent to trigger others' backdoors efficiently. Finally, we study the defense methods against the attack and provide insights for the deployment of multiagent systems.

Decentralized learning (DL) has proven to be effective for privacy-preserving next point-of-interest (POI) recommendation by sharing check-in information among users and collaboratively training on-device models. Existing works, however, simply assume that users tend to share check-ins with neighbors of short geographical distance or similar preferences yet ignore users’ actual willingness to share the information (WSI), causing potential privacy concerns. As such, we present a WSI-guided hierarchical DL framework for next POI recommendation (WHDL-Rec) to seek enhanced privacy protection with recommendation accuracy assured. In particular, WHDL-Rec first performs hierarchical data segregation to partition the private and public user data. It then accords to the server-client architecture, where the server exploits the public data to automatically learn users’ WSI w.r.t. check-ins and capture global user behavior patterns for recommendation accuracy maintenance; and the clients fuse the learned global patterns with the local private data for personalized on-device next POI recommendation, whereby WSI-guided collaborative learning is conducted with more secure check-in sharing. Extensive experiments on three real-world datasets demonstrate the efficacy of WHDL-Rec in delivering more accurate and privacy-preserved recommendations.

Identifying the source of an epidemic involves inferring its origin based on observations of the infected population, where timely isolation of the source is critical to mitigating outbreak spread. Existing approaches predominantly assume static network topologies, yet real-world networks exhibit dynamic changes in nodes and edges over time, significantly challenging conventional source detection methods designed for static networks. To address this limitation, we propose a dynamic network source detection framework based on aMulti-observationSourceFusionSpatio-TemporalGraphConvolutionalNetwork (MSF-STGCN). Our method first partitions epidemic data into multi-frame static graphs, distinguishing between observed and unobserved node states relative to the target detection time. Next, we leverage graph neural networks to extract spatio-temporal features from multi-snapshot sequence data. Finally, we introduce a dynamic message-passing algorithm to process unobserved network components and integrate the results with multi-snapshot spatio-temporal features for accurate source identification. Extensive experiments on synthetic networks and real-world datasets demonstrate the superior performance of our approach compared to state-of-the-art baselines.

Large language models (LLMs) provide a promising way for accurate session-based recommendation (SBR), but they demand substantial computational time and memory. Knowledge distillation (KD)-based methods can alleviate these issues by transferring the knowledge to a small student, which trains a student based on the predictions of a cumbersome teacher. However, these methods encounter difficulties for LLM-based KD in SBR. 1) It is expensive to make LLMs predict for all instances in KD. 2) LLMs may make ineffective predictions for some instances in KD, e.g., incorrect predictions for hard instances or similar predictions as existing recommenders for easy instances. In this paper, we propose an active LLM-based KD method in SBR, contributing to sustainable AI. To efficiently distill knowledge from LLMs with limited cost, we propose to extract a small proportion of instances predicted by LLMs. Meanwhile, for a more effective distillation, we propose an active learning strategy to extract instances that are as effective as possible for KD from a theoretical view. Specifically, we first formulate gains based on potential effects (e.g., effective, similar, and incorrect predictions by LLMs) and difficulties (e.g., easy or hard to fit) of instances for KD. Then, we propose to maximize the minimal gains of distillation to find the optimal selection policy for active learning, which can largely avoid extracting ineffective instances in KD. Experiments on real-world datasets show that our method significantly outperforms state-of-the-art methods for SBR.

Emerging advancements in large language models (LLMs) show significant potential for enhancing recommendations. However, prompt-based methods often struggle to find ideal prompts without task-specific feedback, while fine-tuning-based methods are hindered by high computational demands and dependence on open-source backbones. To address these challenges, we propose a Reflective Reinforcement Large Language Model (Re2LLM) for session-based recommendation, which refines LLMs to generate and utilize specialized knowledge effectively and efficiently. Specifically, we first devise the Reflective Exploration Module to extract and present knowledge in a form that LLMs can easily process. This module enables LLMs to reflect on their recommendation mistakes and construct a hint knowledge base to rectify them effectively. Next, we design the Reinforcement Utilization Module to train a lightweight retrieval agent that elicits correct LLM reasoning. This module recognizes hints as signals to facilitate LLM recommendations and learns to select appropriate hints from the constructed knowledge base using task-specific feedback efficiently. Lastly, we conduct experiments on real-world datasets and demonstrate the superiority of our Re2LLM over state-of-the-art methods.

Large vision-language models (LVLMs) have shown remarkable capabilities in interpreting visual content. While existing works demonstrate these models’ vulnerability to deliberately placed adversarial texts, such texts are often easily identifiable as anomalous. In this paper, we present the first approach to generate scene-coherent typographic adversarial attacks that mislead advanced LVLMs while maintaining visual naturalness through the capability of the LLM-based agent. Our approach addresses three critical questions: what adversarial text to generate, where to place it within the scene, and how to integrate it seamlessly. We propose a training-free, multi-modal LLM-driven scene-coherent typographic adversarial planning (SceneTAP) that employs a three-stage process: scene understanding, adversarial planning, and seamless integration. The SceneTAP utilizes chain-of-thought reasoning to comprehend the scene, formulate effective adversarial text, strategically plan its placement, and provide detailed instructions for natural integration within the image. This is followed by a scene-coherent TextDiffuser that executes the attack using a local diffusion mechanism. We extend our method to real-world scenarios by printing and placing generated patches in physical environments, demonstrating its practical implications. Extensive experiments show that our scene-coherent adversarial text successfully misleads state-of-the-art LVLMs, including ChatGPT-4o, even after capturing new images of physical setups. Our evaluations demonstrate a significant increase in attack success rates while maintaining visual naturalness and contextual appropriateness. This work highlights vulnerabilities in current vision-language models to sophisticated, scene-coherent adversarial attacks and provides insights into potential defense mechanisms. We release our code at https://github.com/tsingqguo/scenetap.

The task of reconstructing and rendering moving humans from monocular videos, particularly when occlusions are present, is fraught with difficulty due to insufficient visual information. Existing approaches struggle with two primary issues in delivering complete and high-fidelity rendering: the reliance on precise geometry constraints, often failing to account for occluded body parts, and the insufficient observation of unseen body parts, resulting in inconsistent reconstructions. To address these limitations, we introduce OcSplats, a deformable 3D gaussian splatting based method tailored for rendering humans in highly occluded scenarios using prior knowledge. OcSplats designs a human body prior-based geometry completion module to recover the occluded human geometry, ensuring complete reconstruction and rendering. Additionally, OcSplats employs a multi-view diffusion prior to regularize human reconstruction pipeline at novel camera poses beyond those in the occluded monocular video. We evaluate OcSplats on the ZJU-MoCap dataset and challenging OcMotion sequences, and experimental results demonstrate that OcSplats significantly outperforms existing state-of-the-art methods in rendering occluded humans.

Combinatorial optimization (CO) is fundamental to several realworld applications, from logistics and scheduling to hardware design and resource allocation.Deep reinforcement learning (RL) has recently shown significant benefits in solving CO problems, reducing reliance on domain expertise and improving computational efficiency.However, the absence of a unified benchmarking framework leads to inconsistent evaluations, limits reproducibility, and increases engineering overhead, raising barriers to adoption for new researchers.To address these challenges, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 27 CO problem environments and 23 state-of-the-art baselines.Built on efficient software libraries and best practices in implementation, RL4CO features modularized implementation and flexible configurations of diverse environments, policy architectures, RL algorithms, and utilities with extensive documentation.RL4CO helps researchers build on existing successes while exploring and developing their own designs, facilitating the entire research process by decoupling science from heavy engineering.We finally provide extensive benchmark studies to inspire new insights and future work.RL4CO has already attracted numerous researchers in the community and is open-sourced at https://github.com/ai4co/rl4co 1 .

New-items play a crucial role in recommender systems (RSs) for delivering fresh and engaging user experiences. However, traditional methods struggle to effectively recommend new-items due to their short exposure time and limited interaction records, especially in dynamic recommender systems (DRSs) where new-items get continuously introduced and users' preferences evolve over time. This leads to significant unfairness towards new-items, which could accumulate over the successive model updates, ultimately compromising the stability of the entire system. Therefore, we propose FairAgent, a reinforcement learning (RL)-based new-item fairness enhancement framework specifically designed for DRSs. It leverages knowledge distillation to extract collaborative signals from traditional models, retaining strong recommendation capabilities for old-items. In addition, FairAgent introduces a novel reward mechanism for recommendation tailored to the characteristics of DRSs, which consists of three components: 1) a new-item exploration reward to promote the exposure of dynamically introduced new-items, 2) a fairness reward to adapt to users' personalized fairness requirements for new-items, and 3) an accuracy reward which leverages users' dynamic feedback to enhance recommendation accuracy. Extensive experiments on three public datasets and backbone models demonstrate the superior performance of FairAgent. The results present that FairAgent can effectively boost new-item exposure, achieve personalized new-item fairness, while maintaining high recommendation accuracy.

Social recommendations (SRs) aim to enhance preference modeling by integrating social networks. However, their effectiveness is mainly constrained by two factors: the noisy social connections that may not reflect shared interests, and the limited number of social connections for most users, which hampers the system's ability to fully leverage social influence. Therefore, it is essential to perform social network refinement by removing noisy connections and adding meaningful ones for robust SRs. Inspired by the denoising capability of generative diffusion models, we propose a Model-Agnostic Social Network Refinement framework with Diffusion Models for Robust Social Recommendation (ARD-SR). Specifically, in the forward process, we corrupt the social network by progressively adding position-specific Gaussian noise calibrated to the user preference similarity, better simulating how the social network responds to noise perturbations. The reverse process learns to denoise, guided by each user's neighborhood preferences from the SR backbone, generating a tailored social network aligned with each user's preference for establishing connections. For effective learning, we design a curriculum-based training mechanism that progressively introduces challenging samples characterized by high sparsity or high noise levels. Finally, ARD-SR and the SR backbone are alternately trained, ensuring a continuous mutual enhancement between the social network refinement and the backbone's user representation learning. To further enhance the quality of the refined social network, (1) we introduce a preference-guided flip operation during inference to improve the input quality; and (2) we modify social connections based on the exponential weighted moving average of ARD-SR's predictions across epochs to reduce fluctuations. Experiments on three datasets show that ARD-SR significantly improves SR performance across multiple SR backbones. The code is released at https://github.com/sunyc123r/ARD-SR.

The communication network in a tunnel construction site facilitates real-time data exchange, and serves as a backbone for successfully executing construction projects. However, the long and closed spaces, irregular surfaces, and variable topology as tunnel excavation impose rigorous limitations on signal propagation, communication quality and coverage. To alleviate the realistic issues, we introduce a holistic three-phase cooperative relay scheme based on 5G New Radio (NR) vehicle-to-everything (V2X) architecture, which can extend the communication range and enhance network throughput. We theoretically derive the outage probability of the entire cooperative relaying process from source to destination, and quantify the impact of relaying on construction workflow with relay cost. To minimize the outage probability and relay cost, we formulate a cooperative relay strategies optimization problem and transform the solving procedure into a Markov decision process (MDP). We design a hybrid hierarchical proximal policy optimization (HH-PPO) reinforcement learning method to solve the MDP, which consists of two discrete actor networks, two continuous actor networks, and two critic networks. The hybrid structure enables HH-PPO to tackle the mixed action space, and the hierarchical structure enables adaptive and contextual actions generation by integrating the discrete network outputs into the continuous actor network. Simulation results validate the effectiveness of the HH-PPO algorithm with faster convergence speed, and show superior performance in terms of lower, stable outage probability and relay cost satisfaction compared with another benchmark.

Competitive opinion maximization (COM) aims to determine some individuals (i.e., seed nodes) from social networks, propagating the desired opinions toward a target entity to their neighbors through social relationships when facing with its competitors (components) and maximize the opinion spread after the specific time. Current studies on COM are still in its infancy, while the only work merely considers the scenario that the strategy of competitors is known but ignores the unknown scenario. In addition, previous studies on COM cannot easily address the situation where some users might dynamically change their opinions. To address the COM issue, we investigate the multistage COM and propose a brand-new Q-learning-based opinion maximization framework (QOMF). Our QOMF consists of two components: dynamic opinion propagation and seeding process. We formulate the COM problem by maximizing relative effective opinions. To produce a dynamic opinion series more realistically, we design an opinion propagation model by joining the activation process and a dynamic opinion process. Moreover, we also verify that the opinion propagation model can reach convergence within finite iterations. To acquire the seed nodes, we design a multistage Q-learning seeding scheme by considering known and unknown competitor strategies, respectively. Experimental results on three real datasets demonstrate that the proposed method outperforms the benchmarks on reaching relatively effective opinions.

Opponent modeling has proven effective in enhancing the decision-making of the controlled agent by constructing models of opponent agents. However, existing methods often rely on access to the observations and actions of opponents, a requirement that is infeasible when such information is either unobservable or challenging to obtain. To address this issue, we introduce distributional opponent-aided multiagent actor-critic (DOMAC), the first speculative opponent modeling algorithm that relies solely on local information (i.e., the controlled agent's observations, actions, and rewards). Specifically, the actor maintains a speculated belief about the opponents using the tailored speculative opponent models that predict the opponents' actions using only local information. Moreover, DOMAC features distributional critic models that estimate the return distribution of the actor's policy, yielding a more fine-grained assessment of the actor's quality. This thus more effectively guides the training of the speculative opponent models that the actor depends upon. Furthermore, we formally derive a policy gradient theorem with the proposed opponent models. Extensive experiments under eight different challenging multiagent benchmark tasks within the MPE, Pommerman, and starcraft multiagent challenge (SMAC) demonstrate that our DOMAC successfully models opponents' behaviors and delivers superior performance against state-of-the-art (SOTA) methods with a faster convergence speed.

With the extensive adoption of information technology, tunnel construction is experiencing a rapid digital transformation. Integrating powerful direct communication among construction equipment (CE) facilitates real-time data exchange, promoting collaborative operations among CE. Concurrent execution of multiple construction procedures leads to a significant rise in the amount of CE and communication links, resulting in resource competition. However, this competition is aimed at enhancing collaboration. To address this inherently contradictory issue, we propose a hierarchical resource management framework and align communication quality of service (QoS) to construction efficiency using construction procedure coherence degree (CPCD) based on age of information (AoI). By formulating resource management as a stochastic optimization problem, a suitable online two-level deep reinforcement learning algorithm referred to as diffusion based soft actor critic (DSAC)-QMIX is designed to derive the radio resource allocation strategies. DSAC is responsible for orchestrating spectrum inter-fleets at the high-level, and QMIX makes the resource management and power control decision for each CE at the low-level. Simulation results validate the effectiveness of the DSAC-QMIX algorithm with comparable transmission rate, and show superior performance in terms of CPCD satisfaction compared with other benchmarks.

Although recent personalization methods have democratized high-resolution image synthesis by enabling swift concept acquisition with minimal examples and lightweight computation, they also present an exploitable avenue for highly accessible backdoor attacks. This paper investigates a critical and unexplored aspect of text-to-image (T2I) diffusion models - their potential vulnerability to backdoor attacks via personalization. By studying the prompt processing of popular personalization methods (epitomized by Textual Inversion and DreamBooth), we have devised dedicated personalization-based backdoor attacks according to the different ways of dealing with unseen tokens and divide them into two families: nouveau-token and legacy-token backdoor attacks. In comparison to conventional backdoor attacks involving the fine-tuning of the entire text-to-image diffusion model, our proposed personalization-based backdoor attack method can facilitate more tailored, efficient, and few-shot attacks. Through comprehensive empirical study, we endorse the utilization of the nouveau-token backdoor attack due to its impressive effectiveness, stealthiness, and integrity, markedly outperforming the legacy-token backdoor attack.

Recommending suitable jobs to users is a critical task in online recruitment platforms. While existing job recommendation methods encounter challenges such as the low quality of users' resumes, which hampers their accuracy and practical effectiveness.With the rapid development of large language models (LLMs), utilizing the rich external knowledge encapsulated within them, as well as their powerful reasoning capabilities, is a promising way to complete users' resumes for more accurate recommendations. However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion. In this paper, we propose a novel LLM-based approach for job recommendation. To alleviate the limitation of fabricated generation for LLMs, we extract accurate and valuable information beyond users' self-description, which helps the LLMs better profile users for resume completion. Specifically, we not only extract users' explicit properties (e.g., skills, interests) from their self-description but also infer users' implicit characteristics from their behaviors for more accurate and meaningful resume completion. Nevertheless, some users still suffer from few-shot problems, which arise due to scarce interaction records, leading to limited guidance for high-quality resume generation. To address this issue, we propose aligning unpaired low-quality with high-quality generated resumes by Generative Adversarial Networks (GANs), which can refine the resume representations for better recommendation results. Extensive experiments on three large real-world recruitment datasets demonstrate the effectiveness of our proposed method.

Continual learning is a process that involves training learning agents to sequentially master a stream of tasks or classes without revisiting past data. The challenge lies in leveraging previously acquired knowledge to learn new tasks efficiently, while avoiding catastrophic forgetting. Existing methods primarily focus on single domains, restricting their applicability to specific problems. In this work, we introduce a novel approach called Cross-Domain Continual Learning (CDCL) that addresses the limitations of being limited to single supervised domains. Our method combines inter- and intra-task cross-attention mechanisms within a compact convolutional network. This integration enables the model to maintain alignment with features from previous tasks, thereby delaying the data drift that may occur between tasks, while performing unsupervised cross-domain (UDA) between related domains. By leveraging an intra-task-specific pseudo-labeling method, we ensure accurate input pairs for both labeled and unlabeled samples, enhancing the learning process. To validate our approach, we conduct extensive experiments on public UDA datasets, showcasing its positive performance on cross-domain continual learning challenges. Additionally, our work introduces incremental ideas that contribute to the advancement of this field. We make our code and models available to encourage further exploration and reproduction of our results: https://github.com/IvsucramlCDCL

Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics. However, their performance is still far from optimality, mainly because the underlying graph representation scheme is unsuitable for modelling partial solutions at each construction step. This paper proposes a novel DRL-guided improvement heuristic for solving JSSP, where graph representation is employed to encode complete solutions. We design a Graph-Neural-Network-based representation scheme, consisting of two modules to effectively capture the information of dynamic topology and different types of nodes in graphs encountered during the improvement process. To speed up solution evaluation during improvement, we present a novel message-passing mechanism that can evaluate multiple solutions simultaneously. We prove that the computational complexity of our method scales linearly with problem size. Experiments on classic benchmarks show that the improvement policy learned by our method outperforms state-of-the-art DRL-based methods by a large margin.

Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, making them less generic and practical. In this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants simultaneously. Specifically, we propose a multi-task vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in computation. We further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational complexity. Experimentally, our method significantly promotes zero-shot generalization performance on 10 unseen VRP variants, and showcases decent results on the few-shot setting and real-world benchmark instances. We further conduct extensive studies on the effect of MoE configurations in solving VRPs, and observe the superiority of hierarchical gating when facing out-of-distribution data. The source code is available at: https://github.com/RoyalSkye/Routing-MVMoE.

Recently, much effort has been devoted to modeling users' multi-interests (aka multi-faceted preferences) based on their behaviors, aiming to accurately capture users' complex preferences. Existing methods attempt to model each interest of users through a distinct representation, but these multi-interest representations easily collapse into similar ones due to a lack of effective guidance. In this paper, we propose a generic multi-interest method for sequential recommendation, achieving disentangled representation learning of diverse interests technically and theoretically. To alleviate the collapse issue of multi-interests, we propose to conduct item partition guided by their likelihood of being co-purchased in a global view. It can encourage items in each group to focus on a discriminated interest, thus achieving effective disentangled learning of multi-interests. Specifically, we first prove the theoretical connection between item partition and spectral clustering, demonstrating its effectiveness in alleviating item-level and facet-level collapse issues that hinder existing disentangled methods. To efficiently optimize this problem, we then propose a Markov Random Field (MRF)-based method that samples small-scale sub-graphs from two separate MRFs, thus it can be approximated with a cross-entropy loss and optimized through contrastive learning. Finally, we perform multi-task learning to seamlessly align item partition learning with multi-interest modeling for more accurate recommendation. Experiments on three real-world datasets show that our method significantly outperforms state-of-the-art methods and can flexibly integrate with existing multi-interest models as a plugin to enhance their performances.

Social Recommendation (SR) typically exploits neighborhood influence in the social network to enhance user preference modeling. However, users' intricate social behaviors may introduce noisy social connections for user modeling and harm the models' robustness. Existing solutions to alleviate social noise either filter out the noisy connections or generate new potential social connections. Due to the absence of labels, the former approaches may retain uncertain connections for user preference modeling while the latter methods may introduce additional social noise. Through data analysis, we discover that (1) social noise likely comes from the connected users with low preference similarity; and (2) Opinion Leaders (OLs) play a pivotal role in influence dissemination, surpassing high-similarity neighbors, regardless of their preference similarity with trusting peers. Guided by these observations, we propose a novel Self-Supervised Denoising approach through Independent Cascade Graph Augmentation, for more robust SR. Specifically, we employ the independent cascade diffusion model to generate an augmented graph view, which traverses the social graph and activates the edges in sequence to simulate the cascading influence spread. To steer the augmentation towards a denoised social graph, we (1) introduce a hierarchical contrastive loss to prioritize the activation of OLs first, followed by high-similarity neighbors, while weakening the low-similarity neighbors; and (2) integrate an information bottleneck based contrastive loss, aiming to minimize mutual information between original and augmented graphs yet preserve sufficient information for improved SR. Experiments conducted on two public datasets demonstrate that our model outperforms the state-of-the-art while also exhibiting higher robustness to different extents of social noise.

Despite enjoying desirable efficiency and reduced reliance on domain expertise, existing neural methods for vehicle routing problems (VRPs) suffer from severe robustness issues — their performance significantly deteriorates on clean instances with crafted perturbations. To enhance robustness, we propose an ensemble-based *Collaborative Neural Framework (CNF)* w.r.t. the defense of neural VRP methods, which is crucial yet underexplored in the literature. Given a neural VRP method, we adversarially train multiple models in a collaborative manner to synergistically promote robustness against attacks, while boosting standard generalization on clean instances. A neural router is designed to adeptly distribute training instances among models, enhancing overall load balancing and collaborative efficacy. Extensive experiments verify the effectiveness and versatility of CNF in defending against various attacks across different neural VRP methods. Notably, our approach also achieves impressive out-of-distribution generalization on benchmark instances.

Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feasibility masking, they struggle with handling complex constraints, especially when obtaining the masking itself is NP-hard. In this paper, we propose a novel Proactive Infeasibility Prevention (PIP) framework to advance the capabilities of neural methods towards more complex VRPs. Our PIP integrates the Lagrangian multiplier as a basis to enhance constraint awareness and introduces preventative infeasibility masking to proactively steer the solution construction process. Moreover, we present PIP-D, which employs an auxiliary decoder and two adaptive strategies to learn and predict these tailored masks, potentially enhancing performance while significantly reducing computational costs during training. To verify our PIP designs, we conduct extensive experiments on the highly challenging Traveling Salesman Problem with Time Window (TSPTW), and TSP with Draft Limit (TSPDL) variants under different constraint hardness levels. Notably, our PIP is generic to boost many neural methods, and exhibits both a significant reduction in infeasible rate and a substantial improvement in solution quality.

Most existing denoising recommendation methods alleviate noisy implicit feedback (user behaviors) through mainly empirical studies. However, such studies may lack theoretical explainability and fail to model comprehensive noise patterns, which hinders the understanding and capturing of different noise patterns that affect users’ behaviors. Thus, we propose to capture comprehensive noise patterns through theoretical and empirical analysis for more effective denoising, where users’ behaviors are divided into willingness and action phases to disentangle independent noise patterns. Willingness refers to the user’s intent to interact with an item, which may not lead to actual interaction due to different factors such as misclicking. Action denotes the user’s actual interaction with an item. Our analysis unveils that (1) in the willingness phase, high uncertainty in the user’s willingness to interact with the item can lead to high expectation loss which aligns with the findings of existing denoising methods; and (2) in the action phase, higher user-specific inconsistency between willingness and action not only leads to more noise in the user’s overall behaviors but also makes it harder to distinguish between true and noisy behaviors. Inspired by these findings, we propose a Unified Denoising Training (UDT) method for recommendation. To alleviate uncertainty in the willingness phase, we lower the importance of the user-item interaction with high willingness uncertainty recognized by high loss. To ease the inconsistency in the action phase, we lower the importance for users with high user-specific inconsistency as it may lead to noisier behaviors. Then, we increase the importance gap between the clean and noisy behaviors for users with low user-specific inconsistency as their behaviors are more distinguishable. Extensive experiments on three real-world datasets show that our proposed UDT outperforms state-of-the-art denoising recommendation methods.

Recommender systems tend to excessively expose longer-standing items, resulting in significant unfairness to new items with little interaction records, despite they may possess potential to attract considerable amount of users. The existing fairness-based solutions do not specifically consider the exposure fairness of new items, for which a systematic definition also lacks, discouraging the promotion of new items or contents. In this work, we introduce a multi-degree new-item exposure fairness definition, which considers item entry-time, and also is configurable regarding different fairness requirements. We then propose a configurable new-item fairness-aware framework named CNIF, which employs two-stage training where fairness degrees are incorporated for guidance. Extensive experiments on multiple popular datasets and backbone models demonstrate that CNIF can effectively enhance fairness of the existing models regarding the exposure resources of new items (including the brand-new items with no interaction). Specifically, CNIF demonstrates a substantial advancement with a 65.59% improvement in fairness metric and a noteworthy 9.97% improvement in recommendation accuracy compared to backbone models on the KuaiRec dataset. In comparison to various fairness-based solutions, it stands out by achieving the best trade-off between fairness and recommendation accuracy, surpassing the best baseline by 14.20%.

Artificial neural networks, celebrated for their human-like cognitive learning abilities, often encounter the well-known catastrophic forgetting (CF) problem, where the neural networks lose the proficiency in previously acquired knowledge. Despite numerous efforts to mitigate CF, it remains the significant challenge particularly in complex changing environments. This challenge is even more pronounced in cross-domain adaptation following the continual learning (CL) setting, which is a more challenging and realistic scenario that is under-explored. To this end, this article proposes a cross-domain CL approach making possible to deploy a single model in such environments without additional labelling costs. Our approach, namely continual learning approach for many processes (CLAMP), integrates a class-aware adversarial domain adaptation strategy to align a source domain and a target domain. An assessor-guided learning process is put forward to navigate the learning process of a base model assigning a set of weights to every sample controlling the influence of every sample and the interactions of each loss function in such a way to balance the stability and plasticity dilemma thus preventing the CF problem. The first assessor focuses on the negative transfer problem rejecting irrelevant samples of the source domain while the second assessor prevents noisy pseudo labels of the target domain. Both assessors are trained in the meta-learning approach using random transformation techniques and similar samples of the source domain. Theoretical analysis and extensive numerical validations demonstrate that CLAMP significantly outperforms established baseline algorithms across all experiments by at least 10% margin.

Among various value decomposition-based multiagent reinforcement learning (MARL) algorithms, the overall performance of the multiagent system is represented by a scalar global Q value and optimized by minimizing the temporal difference (TD) error with respect to that global Q value. However, the global Q value cannot accurately model the distributed dynamics of the multiagent system, since it is only a simplified representation for different individual Q values of agents. To explicitly consider the correlations between different cooperative agents, in this article, we propose a distributional framework and construct a practical model called distributional multiagent cooperation (DMAC) from a novel distributional perspective. Specifically, in DMAC, we view the individual Q value for the executed action of a random agent as a value distribution, whose expectation can further represent the overall performance. Then, we employ distributional RL to minimize the difference between the estimated distribution and its target for the optimization. The advantage of DMAC is that the distributed dynamics of agents can be explicitly modeled, and this results in better performance. To verify the effectiveness of DMAC, we conduct extensive experiments under nine different scenarios of the StarCraft Multiagent Challenge (SMAC). Experimental results show that the DMAC can significantly outperform the baselines with respect to the average median test win rate.

While the encoder-decoder structure is widely used in the recent neural construction methods for learning to solve vehicle routing problems (VRPs), they are less effective in searching solutions due to deterministic feature embeddings and deterministic probability distributions. In this article, we propose the feature embedding refiner (FER) with a novel and generic encoder-refiner-decoder structure to boost the existing encoder-decoder structured deep models. It is model-agnostic that the encoder and the decoder can be from any pretrained neural construction method. Regarding the introduced refiner network, we design its architecture by combining the standard gated recurrent units (GRU) cell with two new layers, i.e., an accumulated graph attention (AGA) layer and a gated nonlinear (GNL) layer. The former extracts dynamic graph topological information of historical solutions stored in a diversified solution pool to generate aggregated pool embeddings that are further improved by the GRU, and the latter adaptively refines the feature embeddings from the encoder with the guidance of the improved pool embeddings. To this end, our FER allows current neural construction methods to not only iteratively refine the feature embeddings for boarder search range but also dynamically update the probability distributions for more diverse search. We apply FER to two prevailing neural construction methods including attention model (AM) and policy optimization with multiple optima (POMO) to solve the traveling salesman problem (TSP) and the capacitated VRP (CVRP). Experimental results show that our method achieves lower gaps and better generalization than the original ones and also exhibits competitive performance to the state-of-the-art neural improvement methods.

Graph Convolutional Networks (GCNs) derive inspiration from recent advances in computer vision, by stacking layers of first-order filters followed by a nonlinear activation function to learn entity or graph embeddings. Although GCNs have been shown to boost the performance of many network analysis tasks, they still face tremendous challenges in learning from Heterogeneous Information Networks (HINs), where relations play a decisive role in knowledge reasoning. What's more, there are multiaspect representations of entities in HINs, and a filter learned in one aspect do not necessarily apply to another. We address these challenges by proposing the Aspect-Aware Graph Attention Network (AGAT), a model that extends GCNs with alternative learnable filters to incorporate entity and relational information. Instead of focusing on learning the general entity embeddings, AGAT learns the adaptive entity embeddings based on prediction scenario. Experiments of link prediction and semi-supervised classification verify the effectiveness of our algorithm.

Recent years have witnessed the great success of group buying (GB) in social e-commerce, opening up a new way of online shopping. In this business model, a user can launch a GB as an initiator to share her interested product with social friends. The GB is clinched once enough friends join in as participants to copurchase the shared product. As such, a successful GB depends on not only whether the initiator can find her interested product but also whether the friends are willing to join in as participants. Most existing recommenders are incompetent in such complex scenario, as they merely seek to help users find their preferred products and cannot help identify potential participants to join in a GB. To this end, we propose a novel joint product-participant recommendation (J2PRec) framework, which recommends both candidate products and participants for maximizing the success rate of a GB. Specifically, J2PRec first designs a relational graph embedding module, which effectively encodes the various relations in GB for learning enhanced user and product embeddings. It then jointly learns the product and participant recommendation tasks under a probabilistic framework to maximize the GB likelihood, i.e., boost the success rate of a GB. Extensive experiments on three real-world datasets demonstrate the superiority of J2PRec for GB recommendation.

A typical benchmark dataset for recommender system (RecSys) evaluation consists of user-item interactions generated on a platform within a time period. The interaction generation mechanism partially explains why a user interacts with (e.g., like, purchase, rate) an item, and the context of when a particular interaction happened. In this study, we conduct a meticulous analysis of the MovieLens dataset and explain the potential impact of using the dataset for evaluating recommendation algorithms. We make a few main findings from our analysis. First, there are significant differences in user interactions at the different stages when a user interacts with the MovieLens platform. The early interactions largely define the user portrait which affect the subsequent interactions. Second, user interactions are highly affected by the candidate movies that are recommended by the platform's internal recommendation algorithm(s). Third, changing the order of user interactions makes it more difficult for sequential algorithms to capture the progressive interaction process. We further discuss the discrepancy between the interaction generation mechanism that is employed by the MovieLens system and that of typical real-world recommendation scenarios. That is, the MovieLens dataset records \(\langle user-MovieLens\rangle\) interactions, but not \(\langle user-movie\rangle\) interactions. All research articles using the MovieLens dataset model the \(\langle user-MovieLens\rangle\) rather than the \(\langle user-movie\rangle\) interactions, making their results less generalizable to many practical recommendation scenarios in real-world settings. In summary, the MovieLens platform demonstrates an efficient and effective way of collecting user preferences to address cold-starts. However, models that achieve excellent recommendation accuracy on the MovieLens dataset may not demonstrate superior performance in practice , for at least two kinds of differences: (1) the differences in the contexts of user-item interaction generation, and (2) the differences in user knowledge about the item collections. While results on MovieLens can be useful as a reference, they should not be solely relied upon as the primary justification for the effectiveness of a recommendation system model.

Conversational recommender system (CRS) enhances the recommender system by acquiring the latest user preference through dialogues, where an agent needs to decide “whether to ask or recommend”, “which attributes to ask”, and “which items to recommend” in each round. To explore these questions, reinforcement learning is adopted in most CRS frameworks. However, existing studies somewhat ignore to consider the connection between the previous rounds and the current round of the conversation, which might lead to the lack of prior knowledge and inaccurate decisions. In this view, we propose to facilitate the connections between different rounds of conversations in a dialogue session through deep transformer-based multi-channel meta-reinforcement learning, so that the CRS agent can decide each action/decision based on previous states, actions, and their rewards. Besides, to better utilize a user’s historical preferences, we propose a more dynamic and personalized graph structure to support the conversation module and the recommendation module. Experiment results on five real-world datasets and an online evaluation with real users in an industrial environment validate the improvement of our method over the state-of-the-art approaches and the effectiveness of our designs.

Current study on next point-of-interest (POI) recommendation mainly explores user sequential transitions with the fine-grained individual-user POI check-in trajectories only, which suffers from the severe check-in data sparsity issue. In fact, coarse-grained signals (i.e., region- and global-level check-ins) in such sparse check-ins would also benefit to augment user preference learning. Specifically, our data analysis unveils that user movement exhibits noticeable patterns w.r.t. the regions of visited POIs. Meanwhile, the global all-user check-ins can help reflect sequential regularities shared by the crowd. We are, therefore, inspired to propose the MCMG: a Multi-Channel next POI recommendation framework with Multi-Granularity signals categorized from two orthogonal perspectives, i.e., fine-coarse grained check-ins at either POI/region level or local/global level. The MCMG is equipped with three modules, namely, global user behavior encoder, local multi-channel (i.e., region, category, and POI channels) encoder, and region-aware weighting strategy. Such design enables MCMG to be capable of capturing both fine- and coarse-grained sequential regularities as well as exploring the dynamic impact of multi-channel by differentiating the check-in patterns w.r.t. visited regions. Extensive experiments on four real-world datasets show that our MCMG significantly outperforms state-of-the-art next POI recommendation approaches.

As the popularity of graph data increases, there is a growing need to count the occurrences of subgraph patterns of interest, for a variety of applications. Many graphs are massive in scale and also fully dynamic (with insertions and deletions of edges), rendering exact computation of these counts to be infeasible. Common practice is, instead, to use a small set of edges as a sample to estimate the counts. Existing sampling algorithms for fully dynamic graphs sample the edges with uniform probability. In this paper, we show that we can do much better if we sample edges based on their individual properties. Specifically, we propose a weighted sampling algorithm called WSD for estimating the subgraph count in a fully dynamic graph stream, which samples the edges based on their weights that indicate their importance and reflect their properties. We determine the weights of edges in a data-driven fashion, using a novel method based on reinforcement learning. We conduct extensive experiments to verify that our technique can produce estimates with smaller errors while often running faster compared with existing algorithms.

Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained and tested on the same task with a fixed size and distribution (of nodes), and hence suffer from limited generalization performance. This paper studies a challenging yet realistic setting, which considers generalization across both size and distribution in VRPs. We propose a generic meta-learning framework, which enables effective training of an initialized model with the capability of fast adaptation to new tasks during inference. We further develop a simple yet efficient approximation method to reduce the training overhead. Extensive experiments on both synthetic and benchmark instances of the traveling salesman problem (TSP) and capacitated vehicle routing problem (CVRP) demonstrate the effectiveness of our method. The code is available at: https://github.com/RoyalSkye/Omni-VRP.

While performing favourably on the independent and identically distributed (i.i.d.) instances, most of the existing neural methods for vehicle routing problems (VRPs) struggle to generalize in the presence of a distribution shift. To tackle this issue, we propose an ensemble-based deep reinforcement learning method for VRPs, which learns a group of diverse sub-policies to cope with various instance distributions. In particular, to prevent convergence of the parameters to the same one, we enforce diversity across sub-policies by leveraging Bootstrap with random initialization. Moreover, we also explicitly pursue inequality between sub-policies by exploiting regularization terms during training to further enhance diversity. Experimental results show that our method is able to outperform the state-of-the-art neural baselines on randomly generated instances of various distributions, and also generalizes favourably on the benchmark instances from TSPLib and CVRPLib, which confirmed the effectiveness of the whole method and the respective designs.

Item consumption and social connection, as common user behaviors in many web applications, have been extensively studied. However, most current works separately perform either item consumption or social link prediction tasks, possibly with the help of the other as an auxiliary signal. Moreover, they merely consider the behaviors in a holistic manner yet neglect the multi-faceted motives behind them. For example, the intention of watching a movie could be killing time or watching it with friends; Likewise, one might connect with others due to friendships or colleagues. To fill this gap, we propose to Disentangle the multi-faceted Motives in each network (i.e., the user-item interaction network and social network) defined respectively by the two types of behaviors, for mutually-enhanced Joint Prediction (DMJP). Specifically, we first learn the disentangled user representations driven by motives of multi-facets in both networks. Thereafter, the mutual influence of the two networks is subtly discriminated at the facet-to-facet level. The fine-grained mutual influence is then exploited asymmetrically to help refine user representations in both networks, with the goal of achieving a mutually-enhanced joint item and social link prediction. Empirical studies on three public datasets showcase the superiority of DMJP over state-of-the-arts (SOTAs) on both tasks.

Recently, neural heuristics based on deep learning have reported encouraging results for solving vehicle routing problems (VRPs), especially on independent and identically distributed (i.i.d.) instances, e.g. uniform. However, in the presence of a distribution shift for the testing instances, their performance becomes considerably inferior. In this paper, we propose a multi-view graph contrastive learning (MVGCL) approach to enhance the generalization across different distributions, which exploits a graph pattern learner in a self-supervised fashion to facilitate a neural heuristic equipped with an active search scheme. Specifically, our MVGCL first leverages graph contrastive learning to extract transferable patterns from VRP graphs to attain the generalizable multi-view (i.e. node and graph) representation. Then it adopts the learnt node embedding and graph embedding to assist the neural heuristic and the active search (during inference) for route construction, respectively. Extensive experiments on randomly generated VRP instances of various distributions, and the ones from TSPLib and CVRPLib show that our MVGCL is superior to the baselines in boosting the cross-distribution generalization performance.

Interactive recommender systems have attracted increasingly research attentions from both academia and industry. This workshop is a half-day event, which provides a forum for researchers and practitioners to discuss recent research progress and novel research directions about interactive recommender systems. The program will include two keynotes and 6 to 8 research paper presentations. The objective of this workshop is to consolidate the recent technical progresses about interactive recommendation, which will be a promising research and development direction for future recommendation technologies. This workshop will attract the attention of researchers from both academia and industry. It aligns with WSDM's spirit of promoting the collaborations between academia and industry.

Recently, one critical issue looms large in the field of recommender systems - there are no effective benchmarks for rigorous evaluation - which consequently leads to unreproducible evaluation and unfair comparison. We, therefore, conduct studies from the perspectives of practical theory and experiments, aiming at benchmarking recommendation for rigorous evaluation. Regarding the theoretical study, a series of hyper-factors affecting recommendation performance throughout the whole evaluation chain are systematically summarized and analyzed via an exhaustive review on 141 papers published at eight top-tier conferences within 2017-2020. We then classify them into model-independent and model-dependent hyper-factors, and different modes of rigorous evaluation are defined and discussed in-depth accordingly. For the experimental study, we release DaisyRec 2.0 library by integrating these hyper-factors to perform rigorous evaluation, whereby a holistic empirical study is conducted to unveil the impacts of different hyper-factors on recommendation performance. Supported by the theoretical and experimental studies, we finally create benchmarks for rigorous evaluation by proposing standardized procedures and providing performance of ten state-of-the-arts across six evaluation metrics on six datasets as a reference for later study. Overall, our work sheds light on the issues in recommendation evaluation, provides potential solutions for rigorous evaluation, and lays foundation for further investigation.

Recently, there is a growing attention on applying deep reinforcement learning (DRL) to solve the 3-D bin packing problem (3-D BPP). However, due to the relatively less informative yet computationally heavy encoder, and considerably large action space inherent to the 3-D BPP, existing DRL methods are only able to handle up to 50 boxes. In this article, we propose to alleviate this issue via a DRL agent, which sequentially addresses three subtasks of sequence, orientation, and position, respectively. Specifically, we exploit a multimodal encoder, where a sparse attention subencoder embeds the box state to mitigate the computation while learning the packing policy, and a convolutional neural network subencoder embeds the view state to produce auxiliary spatial representation. We also leverage an action representation learning in the decoder to cope with the large action space of the position subtask. Besides, we integrate the proposed DRL agent into constraint programming (CP) to further improve the solution quality iteratively by exploiting the powerful search framework in CP. The experiments show that both the sole DRL and hybrid methods enable the agent to solve large-scale instances of 120 boxes or more. Moreover, they both could deliver superior performance against the baselines on instances of various scales.

Deep reinforcement learning (DRL) based recommender systems are suitable for user cold-start problems as they can capture user preferences progressively. However, most existing DRL-based recommender systems are suboptimal, since they use the same policy to suit the dynamics of different users. We reformulate recommendation as a multitask Markov Decision Process, where each task represents a set of similar users. Since similar users have closer dynamics, a task-specific policy is more effective than a single universal policy for all users. To make recommendations for cold-start users, we use a default policy to collect some initial interactions to identify the user task, after which a task-specific policy is employed. We use Q-learning to optimize our framework and consider the task uncertainty by the mutual information regarding tasks. Experiments are conducted on three real-world datasets to verify the effectiveness of our proposed framework.

Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics of aviation. Such a problem involves the interplay among the operations that leads to NP-hard problems with complex constraints. Hence, existing methods for AGH are usually designed with massive domain knowledge but still fail to yield high-quality solutions efficiently. In this paper, we aim to enhance the solution quality and computation efficiency for solving AGH. Particularly, we first model AGH as a multiple-fleet vehicle routing problem (VRP) with miscellaneous constraints including precedence, time windows, and capacity. Then we propose a construction framework that decomposes AGH into sub-problems (i.e., VRPs) in fleets and present a neural method to construct the routing solutions to these sub-problems. In specific, we resort to deep learning and parameterize the construction heuristic policy with an attention-based neural network trained with reinforcement learning, which is shared across all sub-problems. Extensive experiments demonstrate that our method significantly outperforms classic meta-heuristics, construction heuristics and the specialized methods for AGH. Besides, we empirically verify that our neural method generalizes well to instances with large numbers of flights or varying parameters, and can be readily adapted to solve real-time AGH with stochastic flight arrivals. Our code is publicly available at:https://github.com/RoyalSkye/AGH.

We propose a manager-worker framework (the implementation of our model is publically available at:https://github.com/zcaicaros/manager-worker-mtsptwr) based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), i.e., multiple-vehicle TSP with time window and rejections (mTSPTWR), where customers who cannot be served before the deadline are subject to rejections. Particularly, in the proposed framework, a manager agent learns to divide mTSPTWR into sub-routing tasks by assigning customers to each vehicle via a Graph Isomorphism Network (GIN) based policy network. A worker agent learns to solve sub-routing tasks by minimizing the cost in terms of both tour length and rejection rate for each vehicle, the maximum of which is then fed back to the manager agent to learn better assignments. Experimental results demonstrate that the proposed framework outperforms strong baselines in terms of higher solution quality and shorter computation time. More importantly, the trained agents also achieve competitive performance for solving unseen larger instances.

Opinion Maximization (OM) aims at determining a small set of influential individuals, spreading the expected opinions of an object (e.g., product or individual) to their neighbors through the social relationships and eventually producing the largest opinion spread. In previous studies, once the corresponding nodes are activated, their opinions usually keep unchanged, which fails to capture the real scenarios where the opinion of each node on the object can dynamically change over time. In this view, we propose a Dynamic Opinion Maximization Framework (DOMF) to settle the OM problem, which consists of two parts: dynamic opinion formation and adaptive seeding process. Specifically, we formulate the OM problem by maximizing rational opinions, and prove that: 1) the OM problem within a constant ratio is NP-hard, and 2) the objective function does not satisfy the monotonicity and submodularity properties anymore. To model the dynamic opinion issue, we propose adaptive cooperation model based on Q-learning theory, which is proved to be capable of eventually reaching convergence. Moreover, to dynamically generate the initial seed nodes, we design the Multi-stage Heuristic Algorithm (MHA). Experimental results demonstrate that each component of our model is effective, and the proposed approach improves the rational opinion spread.

Online lot sizing for perishable materials in an uncertain environment is a fundamental problem for inventory planning and has been studied for several decades. In this article, we study a novel setting of thelotsizing problem, consideringperishable materials,multiple suppliers,uncertain demands and lead time (LS-PMU), which captures the inventory planning task in real life better than existing lot sizing problems. We present theoretical results of the best possible competitive ratio an online algorithm can achieve for the LS-PMU problem. We then develop a reinforcement learning-based algorithm calledRL4LSto intelligently choose the supplier and decide the order quantity in each time period. We conduct extensive experiments on both real and synthetic datasets to verify thatRL4LSoutperforms existing algorithms in terms of effectiveness and efficiency, e.g.,RL4LSimproves the effectiveness by 44% and runs two orders of magnitude faster than the state-of-the-art algorithmIBFA.

Dispatching vehicle fleets to serve flights is a key task in airport ground handling (AGH). Due to the notable growth of flights, it is challenging to simultaneously schedule multiple types of operations (services) for a large number of flights, where each type of operation is performed by one specific vehicle fleet. To tackle this issue, we first represent the operation scheduling as a complex vehicle routing problem and formulate it as a mixed integer linear programming (MILP) model. Then given the graph representation of the MILP model, we propose a learning assisted large neighborhood search (LNS) method using data generated based on real scenarios, where we integrate imitation learning and graph convolutional network (GCN) to learn a destroy operator to automatically select variables, and employ an off-the-shelf solver as the repair operator to reoptimize the selected variables. Experimental results based on a real airport show that the proposed method allows for handling up to 200 flights with 10 types of operations simultaneously, and outperforms state-of-the-art methods. Moreover, the learned method performs consistently accompanying different solvers, and generalizes well on larger instances, verifying the versatility and scalability of our method.

The uncertain check-ins bring challenges for current static next point-of-interest (POI) recommendation methods. Fortunately, the conversation-based recommendation has been shown the merit of integrating immediate user preference for more accurate recommendations. We, therefore, propose a conversation-based adaptive relational translation (CART) approach for the next POI recommendation over uncertain check-ins. It is equipped with recommender and conversation modules to interactively acquire users' immediate preferences and make dynamic recommendations. Specifically, the recommender built upon the adaptive relational translation method performs location prediction via modeling both users' historical sequential behaviors and the immediate preference received from conversations; the conversation module aims to achieve successful recommendations in fewer conversation turns by learning a conversational strategy, whereby the recommender can be updated via the user response. Extensive experiments on four real-world datasets show the superiority of our proposed CART over the state of the arts.

Recommender models are hard to evaluate, particularly under offline setting. In this article, we provide a comprehensive and critical analysis of the data leakage issue in recommender system offline evaluation. Data leakage is caused by not observing global timeline in evaluating recommenders e.g., train/test data split does not follow global timeline. As a result, a model learns from the user-item interactions that are not expected to be available at the prediction time. We first show the temporal dynamics of user-item interactions along global timeline, then explain why data leakage exists for collaborative filtering models. Through carefully designed experiments, we show that all models indeed recommend future items that are not available at the time point of a test instance, as the result of data leakage. The experiments are conducted with four widely used baseline models—BPR, NeuMF, SASRec, and LightGCN, on four popular offline datasets—MovieLens-25M, Yelp, Amazon-music, and Amazon-electronic, adopting leave-last-one-out data split. 1 We further show that data leakage does impact models’ recommendation accuracy. Their relative performance orders thus become unpredictable with different amount of leaked future data in training. To evaluate recommendation systems in a realistic manner in offline setting, we propose a timeline scheme, which calls for a revisit of the recommendation model design.

Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions. Existing CF-based methods commonly adopt negative sampling to discriminate different items. That is, observed user-item pairs are treated as positive instances; unobserved pairs are considered as negative instances and are sampled under a defined distribution for training. Training with negative sampling on large datasets is computationally expensive. Further, negative items should be carefully sampled under the defined distribution, in order to avoid selecting an observed positive item in the training dataset. Unavoidably, some negative items sampled from the training dataset could be positive in the test set. Recently, self-supervised learning (SSL) , has emerged as a powerful tool to learn a model without negative samples. In this paper, we propose a self-supervised collaborative filtering framework (SelfCF) , that is specially designed for recommender scenario with implicit feedback. The proposed SelfCF framework simplifies Siamese networks and can be easily applied to existing deep-learning based CF models, which we refer to as backbone networks. The main idea of SelfCF is to augment the latent embeddings generated by backbone networks instead of the raw input of user/item ids. We propose and study three embedding perturbation techniques that can be applied to different types of backbone networks including both traditional CF models and graph-based models. The framework enables learning informative representations of users and items without negative samples, and is agnostic to the encapsulated backbones. We conduct experimental comparisons on four datasets, one self-supervised framework, and eight baselines to show that our framework may achieve even better recommendation accuracy than the encapsulated supervised counterpart with a 2×–4× faster training speed. The results also demonstrate that SelfCF can boost up the accuracy of a self-supervised framework BUIR by 17.79% on average and shows competitive performance with baselines.

IoT data exchange plays a vital role in supporting various applications and services with massive IoT devices. However, the existence of malicious devices threatens the integrity and reliability of the exchanged data. Trust management has been used to mitigate the impact of malicious devices in centralized and decentralized architectures. However, most of these traditional trust management systems bear computation, storage, and communication challenges. In this study, we propose a semi-centralized trust management system architecture based on blockchain in both single and multiple domains. The IoT devices are centralized organized by cloud servers who coordinately sustain a rating data ledger within each domain based the proposed rotation based consensus protocol in a decentralized manner to support cross-domain data exchange. A computational trust model is proposed by aggregating the direct and indirect trust information, where we elaborately design decay function, recommendation credibility and adaptable weights so as to calculate the trust value of dynamic malicious devices. Finally, we evaluate the proposed system model in various situations through simulation based experiments and compare it with two classical models in the literature. The experimental results demonstrate the effectiveness of the proposed trust model in identifying malicious devices and mitigating the influence of malicious devices.

Recent deep models for solving routing problems always assume a single distribution of nodes for training, which severely impairs their cross-distribution generalization ability. In this paper, we exploit group distributionally robust optimization (group DRO) to tackle this issue, where we jointly optimize the weights for different groups of distributions and the parameters for the deep model in an interleaved manner during training. We also design a module based on convolutional neural network, which allows the deep model to learn more informative latent pattern among the nodes. We evaluate the proposed approach on two types of well-known deep models including GCN and POMO. The experimental results on the randomly synthesized instances and the ones from two benchmark dataset (i.e., TSPLib and CVRPLib) demonstrate that our approach could significantly improve the cross-distribution generalization performance over the original models.

Existing studies on next point-of-interest (POI) recommendation mainly attempt to learn user preference from the past and current sequential behaviors. They, however, completely ignore the impact of future behaviors on the decision-making, thus hindering the quality of user preference learning. Intuitively, users' next POI visits may also be affected by their multi-step future behaviors, as users may often have activity planning in mind. To fill this gap, we propose a novel Context-aware Future Preference inference Recommender (CFPRec) to help infer user future preference in a self-ensembling manner. In particular, it delicately derives multi-step future preferences from the learned past preference thanks to the periodic property of users' daily check-ins, so as to implicitly mimic user’s activity planning before her next visit. The inferred future preferences are then seamlessly integrated with the current preference for more expressive user preference learning. Extensive experiments on three datasets demonstrate the superiority of CFPRec against state-of-the-arts.

Objective-space decomposition algorithms (ODAs) are widely studied for solving multi-objective integer programs. However, they often encounter difficulties in handling scalarized problems, which could cause infeasibility or repetitive nondominated points and thus induce redundant runtime. To mitigate the issue, we present a graph neural network (GNN) based method to learn the reduction rule in the ODA. We formulate the algorithmic procedure of generic ODAs as a Markov decision process, and parameterize the policy (reduction rule) with a novel two-stage GNN to fuse information from variables, constraints and especially objectives for better state representation. We train our model with imitation learning and deploy it on a state-of-the-art ODA. Results show that our method significantly improves the solving efficiency of the ODA. The learned policy generalizes fairly well to larger problems or more objectives, and the proposed GNN outperforms existing ones for integer programming in terms of test and generalization accuracy.

Multi-task learning (MTL) has been widely utilized in various industrial scenarios, such as recommender systems and search engines. MTL can improve learning efficiency and prediction accuracy by exploiting commonalities and differences across tasks. However, MTL is sensitive to relationships among tasks and may have performance degradation in real-world applications, because existing neural-based MTL models often share the same network structures and original input features. To address this issue, we propose a novel multi-task learning model based on Prototype Feature Extraction (PFE) to balance task-specific objectives and inter-task relationships. PFE is a novel component to disentangle features for multiple tasks. To better extract features from original inputs before gating networks, we introduce a new concept, namely prototype feature center, to disentangle features for multiple tasks. The extracted prototype features fuse various features from different tasks to better learn inter-task relationships. PFE updates prototype feature centers and prototype features iteratively. Our model utilizes the learned prototype features and task-specific experts for MTL. We implement PFE on two public datasets. Empirical results show that PFE outperforms state-of-the-art MTL models by extracting prototype features. Furthermore, we deploy PFE in a real-world recommender system (one of the world’s top-tier short video sharing platforms) to showcase that PFE can be widely applied in industrial scenarios.

Social networks have proven to be effective for high-quality item recommendation. Most social recommenders, however, are insufficient to capture the user preferences over items, as they largely neglect the multi-facet social relations latent in the social network, such as classmates, colleagues, and families. We, therefore, propose a novel disentangled social recommendation (DSR) framework to exploit the multifacet social relations for enhanced item recommendation. Specifically, DSR explicitly disentangles the social relations into multiple facets, and encodes the social influence under each facet into disentangled user embeddings in the social network. The multiple user embeddings are then aggregated via a facet-level attention mechanism, which distinguishes the effective facets for better inferring user interests over items. Extensive experiments show the superiority of DSR against the state-of-the-art methods and its potential in alleviating the data sparsity issue.

Deep reinforcement learning (DRL)-based recommender systems have recently come into the limelight due to their ability to optimize long-term user engagement. A significant challenge in DRL-based recommender systems is the large action space required to represent a variety of items. The large action space weakens the sampling efficiency and thereby, affects the recommendation accuracy. In this article, we propose a DRL-based method called deep hierarchical category-based recommender system (DHCRS) to handle the large action space problem. In DHCRS, categories of items are used to reconstruct the original flat action space into a two-level category-item hierarchy. DHCRS uses two deep Q -networks (DQNs): 1) a high-level DQN for selecting a category and 2) a low-level DQN to choose an item in this category for the recommendation. Hence, the action space of each DQN is significantly reduced. Furthermore, the categorization of items helps capture the users' preferences more effectively. We also propose a bidirectional category selection (BCS) technique, which explicitly considers the category-item relationships. The experiments show that DHCRS can significantly outperform state-of-the-art methods in terms of hit rate and normalized discounted cumulative gain for long-term recommendations.

Existing deep reinforcement learning (DRL)-based methods for solving the capacitated vehicle routing problem (CVRP) intrinsically cope with a homogeneous vehicle fleet, in which the fleet is assumed as repetitions of a single vehicle. Hence, their key to construct a solution solely lies in the selection of the next node (customer) to visit excluding the selection of vehicle. However, vehicles in real-world scenarios are likely to be heterogeneous with different characteristics that affect their capacity (or travel speed), rendering existing DRL methods less effective. In this article, we tackle heterogeneous CVRP (HCVRP), where vehicles are mainly characterized by different capacities. We consider both min-max and min-sum objectives for HCVRP, which aim to minimize the longest or total travel time of the vehicle(s) in the fleet. To solve those problems, we propose a DRL method based on the attention mechanism with a vehicle selection decoder accounting for the heterogeneous fleet constraint and a node selection decoder accounting for the route construction, which learns to construct a solution by automatically selecting both a vehicle and a node for this vehicle at each step. Experimental results based on randomly generated instances show that, with desirable generalization to various problem sizes, our method outperforms the state-of-the-art DRL method and most of the conventional heuristics, and also delivers competitive performance against the state-of-the-art heuristic method, that is, slack induction by string removal. In addition, the results of extended experiments demonstrate that our method is also able to solve CVRPLib instances with satisfactory performance.

Existing network embedding algorithms based on generative adversarial networks (GANs) improve the robustness of node embeddings by selecting high-quality negative samples with the generator to play against the discriminator. Since most of the negative samples can be easily discriminated from positive samples in graphs, their poor competitiveness weakens the function of the generator. Inspired by the sales skills in the market, in this article, we present tripartite adversarial training for network embeddings (TriATNE), a novel adversarial learning framework for learning stable and robust node embeddings. TriATNE consists of three players: 1) producer; 2) seller; and 3) customer. The producer strives to learn the representation of each sample (node pair), making it easy for the customer to differentiate between the positive and the negative, while the seller tries to confuse the customer by selecting realistic-looking samples. The customer, a biased evaluation metric, provides feedback for training the producer and the seller. To further enhance the robustness of node embedding, we model the customer as a two-layer neural network, where each unit in the hidden layer can be regarded as a customer with different preferences. TriATNE also plays against the producer by adjusting the weight of each customer. We test the performance of TriATNE on two common tasks: classification as well as link prediction. The experimental results on various publicly available datasets show that TriATNE can exploit the network structure well.

This paper investigates the reliable shortest path (RSP) problem in Gaussian process (GP) regulated transportation networks. Specifically, the RSP problem that we are targeting at is to minimize the (weighted) linear combination of mean and standard deviation of the path’s travel time. With the reasonable assumption that the travel times of the underlying transportation network follow a multi-variate Gaussian distribution, we propose a Gaussian process path planning (GP3) algorithm to calculate the a priori optimal path as the RSP solution. With a series of equivalent RSP problem transformations, we are able to reach a polynomial time complexity algorithm with guaranteed solution accuracy. Extensive experimental results over various sizes of realistic transportation networks demonstrate the superior performance of GP3 over the state-of-the-art algorithms.

Recently, there is an emerging trend to apply deep reinforcement learning to solve the vehicle routing problem (VRP), where a learnt policy governs the selection of next node for visiting. However, existing methods could not handle well the pairing and precedence relationships in the pickup and delivery problem (PDP), which is a representative variant of VRP. To address this challenging issue, we leverage a novel neural network integrated with a heterogeneous attention mechanism to empower the policy in deep reinforcement learning to automatically select the nodes. In particular, the heterogeneous attention mechanism specifically prescribes attentions for each role of the nodes while taking into account the precedence constraint, i.e., the pickup node must precede the pairing delivery node. Further integrated with a masking scheme, the learnt policy is expected to find higher-quality solutions for solving PDP. Extensive experimental results show that our method outperforms the state-of-the-art heuristic and deep learning model, respectively, and generalizes well to different distributions and problem sizes.

Most existing studies on next point-of-interest (POI) recommendation assume that users deliver certain check-ins over individual POIs. In reality, we typically obtain uncertain check-ins due to the presence of collective POIs, which are gathering places of multiple individual POIs (e.g., shopping malls). On one hand, such uncertain check-ins over collective POIs hinder more accurate next POI recommendation for users due to the transition vanishing issue; on the other hand, the presence of collective POIs poses the challenge for businesses to select which collective POIs to locate in due to complicated competition and cooperation relations between businesses. As such, these collective POIs bring an unprecedented opportunity and necessity on recommendation for both users and businesses. Therefore, we propose novel solutions of location service beneficial for users-businesses. For users, we propose the STSP equipped with category- and location-aware encoders, to deliver more accurate next POI prediction by fusing rich context features. Regarding businesses, we explore their competition and cooperation relations from check-in records, based on which we derive theliving environment(LE) of a business. Insight on site selection for businesses is provided by exploiting the LE, aiming to bring in more profits. Extensive empirical studies demonstrate the efficiency of our solutions.

Recent studies in using deep learning (DL) to solve routing problems focus on construction heuristics, whose solutions are still far from optimality. Improvement heuristics have great potential to narrow this gap by iteratively refining a solution. However, classic improvement heuristics are all guided by handcrafted rules that may limit their performance. In this article, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems. We design a self-attention-based deep architecture as the policy network to guide the selection of the next solution. We apply our method to two important routing problems, i.e., the traveling salesman problem (TSP) and the capacitated vehicle routing problem (CVRP). Experiments show that our method outperforms state-of-the-art DL-based approaches. The learned policies are more effective than the traditional handcrafted ones and can be further enhanced by simple diversifying strategies. Moreover, the policies generalize well to different problem sizes, initial solutions, and even real-world data set.

Incremental update of recommender system models using only newly arrived data may easily cause the model to overfit to the current data. To address this issue without relying on historical data, we propose a sample reweighting method based on prediction performance of previous model on current data. The proposed method effectively alleviates the problem of overfitting and improves the performance of incremental update.

We present a novel deep reinforcement learning method to learn construction heuristics for vehicle routing problems. In specific, we propose a Multi-Decoder Attention Model (MDAM) to train multiple diverse policies, which effectively increases the chance of finding good solutions compared with existing methods that train only one policy. A customized beam search strategy is designed to fully exploit the diversity of MDAM. In addition, we propose an Embedding Glimpse layer in MDAM based on the recursive nature of construction, which can improve the quality of each policy by providing more informative embeddings. Extensive experiments on six different routing problems show that our method significantly outperforms the state-of-the-art deep learning based models.

The e-commerce era is witnessing rising new arrivals of items on e-commerce platforms every day. Identifying potential popular items accurately is of great importance in creating commercial value. Click-Through Rate (CTR) is a general indicator to evaluate item popularity. However, existing methods fail in new arrivals prediction because of sparse item features, missing item statistics and high time complexity of computing for all pairs of users and items. To tackle these challenges, we propose a novel Adversarial Two-tower Neural Network (ATNN) model for new arrivals CTR predictions by introducing an adversarial network to a two-tower network. We design a generator and a discriminator to better learn an item vector based on item profiles without item statistics. We also develop a strategy with an O(1) time complexity for a new item's popularity prediction by constructing a user group and utilizing its mean user vector in a time-efficient manner. We implement ATNN on a largescale real-world dataset from one of the world's largest ecommerce platforms, “Tmall.com”. Empirical results show that ATNN is strongly capable of learning item vectors from item profiles for e-commerce. Furthermore, by introducing multi-task learning technology, we extend ATNN to food delivery service. Experimental results on one popular food delivery platform, “Ele.me”, demonstrate that ATNN can recognize attractive and welcoming new restaurants that have higher Value per Page View (VpPV) and generate more Gross Merchandise Volume (GMV).

We propose a deep reinforcement learning (RL) method to learn large neighborhood search (LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to select a subset of variables at each step, which is reoptimized by an IP solver as the repair operator. However, the combinatorial number of variable subsets prevents direct application of typical RL algorithms. To tackle this challenge, we represent all subsets by factorizing them into binary decisions on each variable. We then design a neural network to learn policies for each variable in parallel, trained by a customized actor-critic algorithm. We evaluate the proposed method on four representative IP problems. Results show that it can find better solutions than SCIP in much less time, and significantly outperform other LNS baselines with the same runtime. Moreover, these advantages notably persist when the policies generalize to larger problems. Further experiments with Gurobi also reveal that our method can outperform this state-of-the-art commercial solver within the same time limit.

We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem. Specifically, we train a Sparse Graph Network (SGN) with supervised learning for edge scores and unsupervised learning for node penalties, both of which are critical for improving the performance of LKH. Based on the output of SGN, NeuroLKH creates the edge candidate set and transforms edge distances to guide the searching process of LKH. Extensive experiments firmly demonstrate that, by training one model on a wide range of problem sizes, NeuroLKH significantly outperforms LKH and generalizes well to much larger sizes. Also, we show that NeuroLKH can be applied to other routing problems such as Capacitated Vehicle Routing Problem (CVRP), Pickup and Delivery Problem (PDP), and CVRP with Time Windows (CVRPTW).

Recommender Systems (RSs) in real-world applications often deal with billions of user interactions daily. To capture the most recent trends effectively, it is common to update the model incrementally using only the newly arrived data. However, this may impede the model’s ability to retain long-term information due to the potential overfitting and forgetting issues. To address this problem, we propose a novel Adaptive Sequential Model Generation (ASMG) framework, which generates a better serving model from a sequence of historical models via a meta generator. For the design of the meta generator, we propose to employ Gated Recurrent Units (GRUs) to leverage its ability to capture the long-term dependencies. We further introduce some novel strategies to apply together with the GRU meta generator, which not only improve its computational efficiency but also enable more accurate sequential modeling. By instantiating the model-agnostic framework on a general deep learning-based RS model, we demonstrate that our method achieves state-of-the-art performance on three public datasets and one industrial dataset.

Bipartite matching between two sets of objects is widely applied in many applications such as crowdsourcing marketplaces, ride-hailing services and logistics.Depending on the applications, different objectives have been proposed, resulting in different matching problems.Among them, one that is recently proposed is online bottleneck matching with delays (OBM-D), where the objective is to optimize the maximum cost of matches and the cost of a match depends on when the match is formed (i.e., it is delay-aware).Existing solutions for OBM-D usually adopt a holding strategy, which holds the objects involved in a match available for a period so as to reduce the chance that a bad match is formed.Nevertheless, existing holding strategies are all based on humancrafted rules thus cannot adapt to the dynamics of how the objects arrive.In this paper, we propose an adaptive holding strategy which is based on reinforcement learning and develop a method called Adaptive-H on top of the new holding strategy.Besides, we prove theoretical results on how good a randomized algorithm could achieve for the OBM-D problem in terms of competitive ratio.We conduct extensive experiments on both real and synthetic datasets to verify that Adaptive-H outperforms existing algorithms in terms of both effectiveness and efficiency.

Routing problems are very important in intelligent transportation systems. Recently, a number of deep learning-based methods are proposed to automatically learn construction heuristics for solving routing problems. However, these methods do not completely follow Bellman's Principle of Optimality since the visited nodes during construction are still included in the following subtasks, resulting in suboptimal policies. In this article, we propose a novel step-wise scheme which explicitly removes the visited nodes in each node selection step. We apply this scheme to two representative deep models for routing problems, pointer network and transformer attention model (TAM), and significantly improve the performance of the original models. To reduce computational complexity, we further propose the approximate step-wise TAM model by modifying one layer of attention. It enables training on larger instances compared to step-wise TAM, and outperforms state-of-the-art deep models with greedy decoding strategy.

As the backbone of communications amongst objects, humans, companies, and administrations, the Internet has become a great integration platform capable of efficiently interconnecting billions of entities, from RFID chips to data centers. This platform provides access to multiple hardware and virtualized resources (servers, networking, storage, applications, connected objects) coming from cloud computing and Internet-of-Things (IoT) infrastructures. From these resources that may be hosted and distributed amongst different providers and tenants, the building and operation of complex and value-added networked systems is enabled.

In the ad hoc teamwork setting, a team of agents needs to perform a task without prior coordination. The most advanced approach learns policies based on previous experiences and reuses one of the policies to interact with new teammates. However, the selected policy in many cases is sub-optimal. Switching between policies to adapt to new teammates' behaviour takes time, which threatens the successful performance of a task. In this paper, we propose AATEAM – a method that uses the attention-based neural networks to cope with new teammates' behaviour in real-time. We train one attention network per teammate type. The attention networks learn both to extract the temporal correlations from the sequence of states (i.e. contexts) and the mapping from contexts to actions. Each attention network also learns to predict a future state given the current context and its output action. The prediction accuracies help to determine which actions the ad hoc agent should take. We perform extensive experiments to show the effectiveness of our method.

Stream classification models for non-stationary environments often assume the immediate availability of data labels. However, in a practical scenario, it is quite natural that the data labels are available only after some temporal lag. This paper explores how a stream classifier model can be made adaptive to such label latency scenario. We propose SkipE-RNN, a self-evolutionary recurrent neural network with dynamically evolving skipped-recurrent-connection for the best utilization of previously observed label information while classifying the current data. When the data label is unavailable, SkipE-RNN uses an auto-learned mapping function to find the best match from the already known data labels and updates the network parameter accordingly. Later, upon availability of true data label, if the previously mapped label is found to be incorrect, SkipE-RNN employs a regularization technique along with the parameter updating process, so as to penalize the model. In addition, SkipE-RNN has inborn power of self-adjusting the network capacity by growing/pruning hidden nodes to cope with the evolving nature of data stream. Rigorous empirical evaluations using synthetic as well as real-world datasets reveal effectiveness of SkipE-RNN in both finitely delayed and infinitely delayed data label scenarios.

Most existing studies on next location recommendation propose to model the sequential regularity of check-in sequences, but suffer from the severe data sparsity issue where most locations have fewer than five following locations. To this end, we propose an Attentional Recurrent Neural Network (ARNN) to jointly model both the sequential regularity and transition regularities of similar locations (neighbors). In particular, we first design a meta-path based random walk over a novel knowledge graph to discover location neighbors based on heterogeneous factors. A recurrent neural network is then adopted to model the sequential regularity by capturing various contexts that govern user mobility. Meanwhile, the transition regularities of the discovered neighbors are integrated via the attention mechanism, which seamlessly cooperates with the sequential regularity as a unified recurrent framework. Experimental results on multiple real-world datasets demonstrate that ARNN outperforms state-of-the-art methods.

Trust and reputation management (TRM) plays an increasingly important role in large-scale online environments such as multi-agent systems (MAS) and the Internet of Things (IoT). One main objective of TRM is to achieve accurate trust assessment of entities such as agents or IoT service providers. However, this encounters an accuracy-privacy dilemma as we identify in this paper, and we propose a framework called Context-aware Bernoulli Neural Network based

Large-scale online promotions, such as Double 11 and Black Friday, are of great value to e-commerce platforms nowadays. Traditional methods are not successful when we aim to maximize global Gross Merchandise Volume (GMV) in the promotion scenarios due to three limitations. The first is that the GMV of sellers varies significantly from daily scenarios to promotions. Second, these methods do not consider explosive demands in promotions, so that a consumer may fail to purchase some popular items due to sellers' limited capacities. Third, the traffic distribution over sellers presents divergence in different channels, thus rendering the performance of the traditional single-channel methods far from optimal in creating commercial values. To address these problems, we design a Multi-Channel Sellers Traffic Allocation (MCSTA) optimization model to obtain optimal page view (PV) distribution concerning global GMV. Then we propose a general constrained non-smooth convex optimization solution with a Multi-Objective Shortest Distance (MOSD) hyperparameter tuning method to solve MCSTA. This is the first work to systematically address this issue in the scenario of large-scale online promotions. The empirical results show that MCSTA achieves significant improvement of GMV by 1.1% based on A/B test during Alibaba's "Global Shopping Festival", one of the world's largest online sales events. Furthermore, we deploy MCSTA in other popular scenarios, including everyday promotion and video live stream service, to showcase that MCSTA can be widely applied in e-commerce and online entertainment services.

Given a graph G and a query vertex q, the topic of community search (CS), aiming to retrieve a dense subgraph of G containing q, has gained much attention. Most existing works focus on undirected graphs which overlooks the rich information carried by the edge directions. Recently, the problem of community search over directed graphs (or CSD problem) has been studied [Fang et al., 2019b]; it finds a connected subgraph containing q, where the in-degree and out-degree of each vertex within the subgraph are at least k and l, respectively. However, existing solutions are inefficient, especially on large graphs. To tackle this issue, in this paper we propose a novel index called D-Forest, which allows a CSD query to be completed within the optimal time cost. We further propose efficient index construction methods. Extensive experiments on six real large graphs show that our index-based query algorithm is up to two orders of magnitude faster than existing solutions.

Studies on next point-of-interest (POI) recommendation mainly seek to learn users' transition patterns with certain historical check-ins. However, in reality, users' movements are typically uncertain (i.e., fuzzy and incomplete) where most existing methods suffer from the transition pattern vanishing issue. To ease this issue, we propose a novel interactive multi-task learning (iMTL) framework to better exploit the interplay between activity and location preference. Specifically, iMTL introduces: (1) temporal-aware activity encoder equipped with fuzzy characterization over uncertain check-ins to unveil the latent activity transition patterns; (2) spatial-aware location preference encoder to capture the latent location transition patterns; and (3) task-specific decoder to make use of the learned latent transition patterns and enhance both activity and location prediction tasks in an interactive manner. Extensive experiments on three real-world datasets show the superiority of iMTL.

Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad of specialized knowledge and often delivering limited performance. In this paper, we propose to automatically learn PDRs via an end-to-end deep reinforcement learning agent. We exploit the disjunctive graph representation of JSSP, and propose a Graph Neural Network based scheme to embed the states encountered during solving. The resulting policy network is size-agnostic, effectively enabling generalization on large-scale instances. Experiments show that the agent can learn high-quality PDRs from scratch with elementary raw features, and demonstrates strong performance against the best existing PDRs. The learned policies also perform well on much larger instances that are unseen in training.

With tremendous amount of recommendation algorithms proposed every year, one critical issue has attracted a considerable amount of attention: there are no effective benchmarks for evaluation, which leads to two major concerns, i.e., unreproducible evaluation and unfair comparison. This paper aims to conduct rigorous (i.e., reproducible and fair) evaluation for implicit-feedback based top-N recommendation algorithms. We first systematically review 85 recommendation papers published at eight top-tier conferences (e.g., RecSys, SIGIR) to summarize important evaluation factors, e.g., data splitting and parameter tuning strategies, etc. Through a holistic empirical study, the impacts of different factors on recommendation performance are then analyzed in-depth. Following that, we create benchmarks with standardized procedures and provide the performance of seven well-tuned state-of-the-arts across six metrics on six widely-used datasets as a reference for later study. Additionally, we release a user-friendly Python toolkit, which differs from existing ones in addressing the broad scope of rigorous evaluation for recommendation. Overall, our work sheds light on the issues in recommendation evaluation and lays the foundation for further investigation. Our code and datasets are available at GitHub (https://github.com/AmazingDD/daisyRec).

Popularity is often included in experimental evaluation to provide areference performance for a recommendation task. To understand how popularity baseline is defined and evaluated, we sample 12 papers from top-tier conferences including KDD, WWW, SIGIR, and RecSys, and 6 open source toolkits. We note that the widely adoptedMostPop baseline simply ranks items based on the number of interactions in the training data. We argue that the current evaluation of popularity (i) does not reflect the popular items at the time when a user interacts with the system, and (ii) may recommend items released after a user's last interaction with the system. On the widely used MovieLens dataset, we show that the performance of popularity could be significantly improved by 70% or more, if we consider the popular itemsat the time point when a user interacts with the system. We further show that, on MovieLens dataset, the users having lower tendencies on movies tend to follow the crowd and rate more popular movies. Movie lovers who rate a large number of movies, rate movies based on their own preferences and interests. Through this study, we call for a re-visit of the popularity baseline in recommender system to better reflect its effectiveness.

Network embeddings aim to learn representations of nodes in a network with both the first- and the high-order proximities preserved. The first-order proximity corresponds to network reconstruction, while the high-order proximity is in tune with network inference. Since the tradeoff between the two proximities varies on scenarios, we propose an adjustable network embedding (ANE) algorithm for adjusting the weight between the first- and the high-order proximities. ANE is based on two hypotheses: 1) nodes in closed triplets are more important than nodes in open triplets and 2) closed triplets with higher degrees are more important. In addition, we change the bidirectional sampling of Word2vec into directional sampling to preserve the frequency of node pairs in the training set. Three common tasks, network reconstruction, link prediction, and classification are conducted on various publicly available data sets to validate the abovementioned statements.

Ratings provided by advisors can help an advisee to make decisions, e.g., which seller to select in e-commerce. Unfair rating attacks-where dishonest ratings are provided to mislead the advisee-impact the accuracy of decision making. Current literature focuses on specific classes of unfair rating attacks, which does not provide a complete picture of the attacks. We provide the first formal study that addresses all attack behavior that is possible within a given system. We propose a probabilistic modeling of rating behavior, and apply information theory to quantitatively measure the impact of attacks. In particular, we can identify the attack with the worst impact. In the simple case, honest advisors report the truth straightforwardly, and attackers rate strategically. In real systems, the truth (or an advisor's view on it) may be subjective, making even honest ratings inaccurate. Although there exist methods to deal with subjective ratings, whether subjectivity influences the effect of unfair rating attacks was an open question. We discover that subjectivity decreases the robustness against attacks.

Incentive mechanisms that assume agents to be fully rational, may fail due to the bounded rationality of agents in practice. It is thus crucial to evaluate to what extent mechanisms can resist agents’ bounded rationality, termed robustness. In this paper, we propose a general empirical framework for robustness evaluation. One novelty of our framework is to develop a robustness formulation that is generally applicable to different types of incentive mechanisms and bounded rationality models. This formulation considers not only the incentives to agents but also the performance of mechanisms. The other novelty lies in converting the empirical robustness computation into a continuum-armed bandit problem, and then developing an efficient solver that has theoretically guaranteed error rate upper bound. We also conduct extensive experiments using various mechanisms to verify the advantages and practicability of our robustness evaluation framework.

The task of user ranking in signed networks, aiming to predict potential friends and enemies for each user, has attracted increasing attention in numerous applications. Existing approaches are mainly extended from heuristics of the traditional models in unsigned networks. They suffer from two limitations: (1) mainly focus on global rankings thus cannot provide effective personalized ranking results, and (2) have a relatively unrealistic assumption that each user treats her neighbors’ social strengths indifferently. To address these two issues, we propose a supervised method based on random walk to learn social strengths between each user and her neighbors, in which the random walk more likely visits “potential friends” and less likely visits “potential enemies”. We learn the personalized social strengths by optimizing on a particularly designed loss function oriented on ranking. We further present a fast ranking method based on the local structure among each seed node and a certain set of candidates. It much simplifies the proposed ranking model meanwhile maintains the performance. Experimental results demonstrate the superiority of our approach over the state-of-the-art approaches.

Given a graph G and a vertex q ∊ G, the community search (CS) problem aims to efficiently find a subgraph of G whose vertices are closely related to q. Communities are prevalent in social and biological networks, and can be used in product advertisement and social event recommendation. In this paper, we study profiled community search (PCS), where CS is performed on a profiled graph. This is a graph in which each vertex has labels arranged in a hierarchical manner. Compared with existing CS approaches, PCS can sufficiently identify vertices with semantic commonalities and thus find more high-quality diverse communities. As a naive solution for PCS is highly expensive, we have developed a tree index, which facilitates efficient and online solutions for PCS.

In this paper, we propose MUSE-RNN, a multilayer self-evolving recurrent neural network model for real-time classification of streaming data. Unlike the existing approaches, MUSE-RNN offers special treatment towards capturing temporal aspects of data stream through its novel recurrent learning approach based on the teacher forcing policy. Novelties here are twofold. First, in contrast to the traditional RNN models, MUSE-RNN has intrinsic ability to self-adjust its capacity by growing and pruning hidden nodes as well as layers, to handle the ever-changing characteristics of data stream. Second, MUSERNN adopts a unique scoring-based layer adaptation mechanism, which makes it capable of recalling prior tasks, with minimum exploitation of network parameters. The performance of MUSERNN is evaluated in comparison with a number of state-of-theart techniques, using seven popular data streams and continual learning problems under prequential test-then-train protocol. Experimental results demonstrate the effectiveness of MUSERNN in stream classification scenario.

In an ad hoc teamwork setting, the team needs to coordinate their activities to perform a task without prior agreement on how to achieve it. The ad hoc agent cannot communicate with its teammates but it can observe their behaviour and plan accordingly. To do so, the existing approaches rely on the teammates' behaviour models. However, the models may not be accurate, which can compromise teamwork. For this reason, we present Ad Hoc Teamwork by Sub-task Inference and Selection (ATSIS) algorithm that uses a sub-task inference without relying on teammates' models. First, the ad hoc agent observes its teammates to infer which sub-tasks they are handling. Based on that, it selects its own sub-task using a partially observable Markov decision process that handles the uncertainty of the sub-task inference. Last, the ad hoc agent uses the Monte Carlo tree search to find the set of actions to perform the sub-task. Our experiments show the benefits of ATSIS for robust teamwork.

Data quality, or sometimes referred to as data credibility, is a critical issue in mobile crowd sensing (MCS) and more generally Internet of Things (IoT). While candidate solutions, such as incentive mechanisms and data mining have been well explored in the literature, the power of crowds has been largely overlooked or under-exploited. In this paper, we propose a cross validation approach which seeks a validating crowd to ratify the contributing crowd in terms of the sensor data contributed by the latter, and uses the validation result to reshape data into a more credible posterior belief of the ground truth. This approach consists of a framework and a mechanism, where the framework outlines a four-step procedure and the mechanism implements it with specific technical components, including a weighted random oversampling (WRoS) technique and a privacy-aware trust-oriented probabilistic push (PATOP2) algorithm. Unlike most prior work, our proposed approach augments rather than redesigning existing MCS systems, and requires minimal effort from the crowd, making it conducive to practical adoption. We evaluate our proposed mechanism using a real-world MCS IoT dataset and demonstrate remarkable (up to 475%) improvement of data quality. In particular, it offers a unified solution to reconciling two disparate needs: reinforcing obscure (weakly recognizable) ground truths and discovering hidden (unrecognized) ground truths.

In real-world project scheduling applications, activity durations are often uncertain. Proactive scheduling can effectively cope with the duration uncertainties, by generating robust baseline solutions according to a priori stochastic knowledge. However, most of the existing proactive approaches assume that the duration uncertainty of an activity is not related to its scheduled start time, which may not hold in many real-world scenarios. In this paper, we relax this assumption by allowing the duration uncertainty to be time-dependent, which is caused by the uncertainty of whether the activity can be executed on each time slot. We propose a stochastic optimization model to find an optimal Partial-order Schedule (POS) that minimizes the expected makespan. This model can cover both the time-dependent uncertainty studied in this paper and the traditional time-independent duration uncertainty. To circumvent the underlying complexity in evaluating a given solution, we approximate the stochastic optimization model based on Sample Average Approximation (SAA). Finally, we design two efficient branch-and-bound algorithms to solve the NP-hard SAA problem. Empirical evaluation confirms that our approach can generate high-quality proactive solutions for a variety of uncertainty distributions.

The Android ecosystem has recently dominated mobile devices. Android app markets, including official Google Play and other third party markets, are becoming hotbeds, where malware originates and spreads. Android malware has been observed to both propagate within markets and spread between markets. If the spread of Android malware between markets can be predicted, market administrators can take appropriate measures to prevent the outbreak of malware and minimize the damages caused by malware. In this paper, we make the first attempt to protect the Android ecosystem by modeling and predicting the spread of Android malware between markets. To this end, we study the social behaviors that affect the spread of malware, model these spread behaviors with multiple epidemic models, and predict the infection time and order among markets for well-known malware families. To achieve an accurate prediction of malware spread, we model spread behaviors in the following fashion: 1) for a single market, we model the within-market malware growth by considering both the creation and removal of malware; 2) for multiple markets, we determine market relevance by calculating the mutual information among them; and 3) based on the previous two steps, we simulate a susceptible infected model stochastically for spread among markets. The model inference is performed using a publicly available well-labeled dataset AndRadar. To conduct extensive experiments to evaluate our approach, we collected a large number (334,782) of malware samples from 25 Android markets around the world. The experimental results show our approach can depict and simulate the growth of Android malware on a large scale, and predict the infection time and order among markets with 0.89 and 0.66 precision, respectively.

Given a graph $G$G and a vertex $q\in G$q∈G, the community search (CS) problem aims to efficiently find a subgraph of $G$G whose vertices are closely related to $q$q. Communities are prevalent in social and biological networks, and can be used in product advertisement and social event recommendation. In this paper, we study profiled community search (PCS), where CS is performed on a profiled graph. This is a graph in which each vertex has labels arranged in a hierarchical manner. Extensive experiments show that PCS can identify communities with themes that are common to their vertices, and is more effective than existing CS approaches. As a naive solution for PCS is highly expensive, we have also developed a tree index, which facilitates efficient and online solutions for PCS.

editorial Free Access Share on Introduction to the Special Section on Trust and AI Editors: Jie Zhang NTU, Singapore NTU, SingaporeView Profile , Jamal Bentahar Concordia University, Canada Concordia University, CanadaView Profile , Rino Falcone ISTC-CNR, Italy ISTC-CNR, ItalyView Profile , Timothy J. Norman University of Southampton, UK University of Southampton, UKView Profile , Murat Şensoy Blue Prism Labs, UK Blue Prism Labs, UKView Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 19Issue 4November 2019 Article No.: 44epp 1–3https://doi.org/10.1145/3365675Online:09 November 2019Publication History 0citation344DownloadsMetricsTotal Citations0Total Downloads344Last 12 Months114Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

Malicious vehicle agents broadcast fake information about traffic events and thereby undermine the benefits of vehicle-to-vehicle communication in vehicular ad-hoc networks (VANETs). Trust management schemes addressing this issue do not focus on effective/fast decision making in reacting to traffic events. We propose a Partially Observable Markov Decision Process (POMDP) based approach to balance the trade-off between information gathering and exploiting actions resulting in faster responses. Our model copes with malicious behavior by maintaining it as part of a small state space, thus is scalable for large VANETs. We also propose an algorithm to learn model parameters in a dynamic behavior setting. Experimental results demonstrate that our model can effectively balance the decision quality and response time while still being robust to sophisticated malicious attacks.

Link prediction in signed social networks is challenging because of the existence and imbalance of the three kinds of social status (positive, negative and no-relation). Furthermore, there are a variety types of no-relation status in reality, e.g., strangers and frenemies, which cannot be well distinguished from the other linked status by existing approaches. In this paper, we propose a novel Framework of Integrating both Latent and Explicit features (FILE), to better deal with the no-relation status and improve the overall link prediction performance in signed networks. In particular, we design two latent features from latent space and two explicit features by extending social theories, and learn these features for each user via matrix factorization with a specially designed ranking-oriented loss function. Experimental results demonstrate the superior of our approach over state-of-the-art methods.

In this paper, we consider the challenging problem of riskaware proactive scheduling with the objective of minimizing robust makespan. State-of-the-art approaches based on probabilistic constrained optimization lead to Mixed Integer Linear Programs that must be heuristically approximated. We optimize the robust makespan via a coherent risk measure, Conditional Value-at-Risk (CVaR). Since traditional CVaR optimization approaches assuming linear spaces does not suit our problem, we propose a general branch-and-bound framework for combinatorial CVaR minimization. We then design an approximate complete algorithm, and employ resource reasoning to enable constraint propagation for multiple samples. Empirical results show that our algorithm outperforms state-of-the-art approaches with higher solution quality.

Sentence compression condenses a sentence while preserving its most important contents. Delete-based models have the strong ability to delete undesired words, while generate-based models are able to reorder or rephrase the words, which are more coherent to human sentence compression. In this paper, we propose Operation Network, a neural network approach for abstractive sentence compression, which combines the advantages of both delete-based and generate-based sentence compression models. The central idea of Operation Network is to model the sentence compression process as an editing procedure. First, unnecessary words are deleted from the source sentence, then new words are either generated from a large vocabulary or copied directly from the source sentence. A compressed sentence can be obtained by a series of such edit operations (delete, copy and generate). Experiments show that Operation Network outperforms state-of-the-art baselines.

The vastly diverse and increasingly autonomous Internet of Things (IoT) devices stress trust management as a critical requirement of IoT. This paper addresses subjectivity as an important issue in trust management for IoT. Subjectivity means that the information provided by each autonomous IoT device, represented by an agent, is likely to have been influenced by the device's individual preference, which can be misleading in trust evaluation. In this paper, we seek to align the potentially subjective information with the information seeker's own subjectivity so that the acquired second-hand information is more useful and personalized. Accordingly, we propose a multiagent subjectivity alignment (MASA) mechanism, which models the subjectivity using a regression technique and exchanges the models among agents as the input to an alignment process. This mechanism substantially counteracts biases incurred by different agents and improves the accuracy of second-hand information fusion as demonstrated by our simulations. In addition, we also conduct experiments using a real-world dataset (MovieLens) which further validates the efficacy of MASA.

Active learning strategies are often used in crowd labeling to improve task assignment. However, these strategies require prohibitive computation time yet still cannot improve the assignment to the utmost, because they simply evaluate each possible assignment and then greedily select the optimal one. In this paper, we first derive an efficient algorithm for assignment evaluation. Then, to overcome the uncertainty of labels, we develop a novel strategy that modulates the scope of the greedy task assignment with posterior uncertainty and keeps the evaluation optimistic. The experiments on two popular worker models and four MTurk datasets show that our strategy achieves the best performance and highest computation efficiency.

Incentive mechanisms for crowdsourcing are designed to incentivize financially self-interested workers to generate and report high-quality labels. Existing mechanisms are often developed as one-shot static solutions, assuming a certain level of knowledge about worker models (expertise levels, costs for exerting efforts, etc.). In this paper, we propose a novel inference aided reinforcement mechanism that acquires data sequentially and requires no such prior assumptions. Specifically, we first design a Gibbs sampling augmented Bayesian inference algorithm to estimate workers' labeling strategies from the collected labels at each step. Then we propose a reinforcement incentive learning (RIL) method, building on top of the above estimates, to uncover how workers respond to different payments. RIL dynamically determines the payment without accessing any ground-truth labels. We theoretically prove that RIL is able to incentivize rational workers to provide high-quality labels both at each step and in the long run. Empirical results show that our mechanism performs consistently well under both rational and non-fully rational (adaptive learning) worker models. Besides, the payments offered by RIL are more robust and have lower variances compared to existing one-shot mechanisms.

Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing methods mainly rely on hand-engineered features from KGs (e.g., meta paths), which requires domain knowledge. This paper presents RKGE, a KG embedding approach that automatically learns semantic representations of both entities and paths between entities for characterizing user preferences towards items. Specifically, RKGE employs a novel recurrent network architecture that contains a batch of recurrent networks to model the semantics of paths linking a same entity pair, which are seamlessly fused into recommendation. It further employs a pooling operator to discriminate the saliency of different paths in characterizing user preferences towards items. Extensive validation on real-world datasets shows the superiority of RKGE against state-of-the-art methods. Furthermore, we show that RKGE provides meaningful explanations for recommendation results.

In online social networks (OSNs), e.g. Facebook, the relationship between users is binary, i.e., either friend (trust) or stranger (distrust). However, in real-world life, people always have different trust relationships with others (e.g., best friend, acquaintance, frenemy). For various applications such as social recommendation and semantic web, it is more worthwhile to know the trust strength between users. In this work, via a unique dataset obtained from a Facebook app and a carefully designed user study, we map trust values with users' online interactions, and thus build personalized trust models. For each individual, we learn her trust model via optimization on a ranking-oriented loss function. Experimental results demonstrate the superior of the proposed approach over state-of-the-art method and the good generalization ability of the approach.

Personalized recommendation for online service systems aims to predict potential demand by analysing user preference. User preference can be inferred from heterogeneous implicit feedback (i.e. various user actions) especially when explicit feedback (i.e. ratings) is not available. However, most methods either merely focus on homogeneous implicit feedback (i.e. target action), e.g., purchase in shopping websites and forward in Twitter, or dispose heterogeneous implicit feedback without the investigation of its speciality. In this paper, we adopt two typical actions in online service systems, i.e., view and like , as auxiliary feedback to enhance recommendation performance, whereby we propose a Bayesian personalized ranking method for heterogeneous implicit feedback (BPRH). Specifically, items are first classified into different types according to the actions they received. Then by analysing the co-occurrence of different types of actions, which is one of the fundamental speciality of heterogeneous implicit feedback systems, we quantify their correlations, based on which the difference of users’ preference among different types of items is investigated. An adaptive sampling strategy is also proposed to tackle the unbalanced correlation among different actions. Extensive experimentation on three real-world datasets demonstrates that our approach significantly outperforms state-of-the-art algorithms.

An incentive mechanism is designed to achieve desired outcomes as Nash equilibrium, by assuming agents to be fully rational. Nevertheless, practical agents may violate this assumption for various reasons, causing mechanisms to fail. Thus, before deploying a mechanism in practice, it is crucial to quantitatively evaluate to what extent the Nash equilibrium can resist different kinds of bounded rationality, termed robustness. In this paper, focusing on Nash equilibrium, we first propose a general robustness formulation as the upper bound of the stable region of equilibrium strategies by generalizing existing bounded rationality models. We also show that different existing robustness formulations of Nash equilibrium can be derived from this general formulation, which verifies the soundness of our formulation. Then, we develop a robustness evaluation framework specifically for incentive mechanisms, of which the key component is the empirical stability test given a certain level of bounded rationality. Finally, the evaluation framework is validated on three typical but distinct incentive mechanisms, and the robustness computation results conform to our theoretical analysis. The comparison also offers us a good reference for making a proper selection among different designs.

In this paper, we introduce a framework for modeling the trustworthiness of peers in the setting of online social networks. In these contexts, it may be important to be filtering the wealth of messages that have been sent, which form the ongoing communication within a large community of users. This is achieved by constructing an intelligent agent that reasons about the message and each peer rater of the message, learning over time to properly gage whether a message is good or bad to show a user, based on message ratings, rater similarity, and rater credibility. Our approach employs a partially observable Markov decision process for trust modeling, moving beyond the more traditional adoption of probabilistic reasoning using beta reputation functions. In addition to outlining the technique in full, we present empirical results to demonstrate the effectiveness of our methods, both in simulations featuring head to head comparisons with competitors, and in the context of some existing online social networks where ground truth data are available.

Arriving on time and total travel time are two important properties for vehicle routing. Existing route guidance approaches always consider them independently, because they may conflict with each other. In this article, we develop a semi-decentralized multiagent-based vehicle routing approach where vehicle agents follow the local route guidance by infrastructure agents at each intersection, and infrastructure agents perform the route guidance by solving a route assignment problem. It integrates the two properties by expressing them as two objective terms of the route assignment problem. Regarding arriving on time, it is formulated based on the probability tail model, which aims to maximize the probability of reaching destination before deadline. Regarding total travel time, it is formulated as a weighted quadratic term, which aims to minimize the expected travel time from the current location to the destination based on the potential route assignment. The weight for total travel time is designed to be comparatively large if the deadline is loose. Additionally, we improve the proposed approach in two aspects, including travel time prediction and computational efficiency. Experimental results on real road networks justify its ability to increase the average probability of arriving on time, reduce total travel time, and enhance the overall routing performance.

The stochastic shortest path problem is of crucial importance for the development of sustainable transportation systems. Existing methods based on the probability tail model seek for the path that maximizes the probability of arriving at the destination before a deadline. However, they suffer from low accuracy and/or high computational cost. We design a novel Q-learning method where the converged Q-values have the practical meaning as the actual probabilities of arriving on time so as to improve accuracy. By further adopting dynamic neural networks to learn the value function, our method can scale well to large road networks with arbitrary deadlines. Experimental results on real road networks demonstrate the significant advantages of our method over other counterparts.

We rethink the link prediction problem in signed social networks by also considering "no-relation" as a future status of a node pair, rather than simply distinguishing positive and negative links proposed in the literature. To understand the underlying mechanism of link formation in signed networks, we propose a feature framework on the basis of a thorough exploration of potential features for the newly identified problem. Grounded on the framework, we also design a trinary classification model, and experimental results show that our method outperforms the state-of-the-art approaches.

Most of the existing proactive scheduling approaches assume the durations of activities can be described by independent random variables that have no relation with time. We deal with the more challenging problem where the duration uncertainty is related to the scheduled time period. We propose a sampling based approach by extending the Consensus method from stochastic optimization. Experimental results show the effectiveness of our approach in solution quality and stability.

Feature hierarchy (FH) has proven to be effective to improve recommendation accuracy. Prior work mainly focuses on the influence of vertically affiliated features (i.e. child-parent) on user-item interactions. The relationships of horizontally organized features (i.e. siblings and cousins) in the hierarchy, however, has only been little investigated. We show in real-world datasets that feature relationships in horizontal dimension can help explain and further model user-item interactions. To fully exploit FH, we propose a unified recommendation framework that seamlessly incorporates both vertical and horizontal dimensions for effective recommendation. Our model further considers two types of semantically rich feature relationships in horizontal dimension, i.e. complementary and alternative relationships. Extensive validation on four real-world datasets demonstrates the superiority of our approach against the state of the art. An additional benefit of our model is to provide better interpretations of the generated recommendations.

Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history are equally relevant to recommendation, which however does not apply in real-world scenarios where user-item interactions can often happen accidentally. More importantly, they learn user and item dynamics separately, thus failing to capture their joint effects on user-item interactions. To better model user and item dynamics, we present the Interacting Attention-gated Recurrent Network (IARN) which adopts the attention model to measure the relevance of each time step. In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions. By doing so, IARN can selectively memorize different time steps of a user's history when predicting her preferences over different items. Our model can therefore provide meaningful interpretations for recommendation results, which could be further enhanced by auxiliary features. Extensive validation on real-world datasets shows that IARN consistently outperforms state-of-the-art methods.

Belief fusion consists of taking into account multiple sources of belief about a domain of interest. This paper describes cumulative and averaging multi-source belief fusion in the formalism of subjective logic, which represent generalisations of binary-source belief fusion operators previously described. The advantage of this approach is that we can model and analyse belief fusion situations involving an arbitrary number of sources.

Different belief sources often provide conflicting evidence, due to e.g. varying source reliability or deliberate deception. Source trust expresses the source reliability as seen by the analyst. In case of conflicting sources the analyst needs a strategy for managing and revising source trust. Intuitively, trust should be reduced for sources that produce advice which is in conflict with the ground truth, or in conflict with the advice from other highly trusted sources. The present paper uses the formalism of subjective logic to describe strategies for source trust revision according to this principle.

Posted-price mechanisms are widely-adopted to decide the price of tasks in popular microtask crowdsourcing. In this paper, we propose a novel posted-price mechanism which not only outperforms existing mechanisms on performance but also avoids their need of a finite price range. The advantages are achieved by converting the pricing problem into a multi-armed bandit problem and designing an optimal algorithm to exploit the unique features of microtask crowdsourcing. We theoretically show the optimality of our algorithm and prove that the performance upper bound can be achieved without the need of a prior price range. We also conduct extensive experiments using real price data to verify the advantages and practicability of our mechanism.

Representation learning (RL) has recently proven to be effective in capturing local item relationships by modeling item co-occurrence in individual user's interaction record. However, the value of RL for recommendation has not reached the full potential due to two major drawbacks: 1) recommendation is modeled as a rating prediction problem but should essentially be a personalized ranking one; 2) multi-level organizations of items are neglected for fine-grained item relationships. We design a unified Bayesian framework MRLR to learn user and item embeddings from a multi-level item organization, thus benefiting from RL as well as achieving the goal of personalized ranking. Extensive validation on real-world datasets shows that MRLR consistently outperforms state-of-the-art algorithms.

Data trustworthiness is a crucial issue in real-world participatory sensing applications. Without considering this issue, different types of worker misbehavior, especially the challenging collusion attacks, can result in biased and inaccurate estimation and decision making. We propose a novel trust-based mixture of Gaussian processes (GP) model for spatial regression to jointly detect such misbehavior and accurately estimate the spatial field. We develop a Markov chain Monte Carlo (MCMC)-based algorithm to efficiently perform Bayesian inference of the model. Experiments using two real-world datasets show the superior robustness of our model compared with existing approaches.

Reputation fraud campaigns (RFCs) distort the reputations of rated items, by generating fake ratings through multiple spammers. One effective way of detecting RFCs is to characterize their collective behaviors based on rating histories.However, these campaigns are constantly evolving and changing tactics to evade detection.For example, they can launch early attacks on the items to quickly dominate the reputations.They can also whitewash themselves through creating new accounts for subsequent attacks.It is thus challenging for existing approaches working on historical data to promptly react to such emerging fraud activities.In this paper, we conduct RFC detection in online fashion, so as to spot campaign activities as early as possible.This leads to a unified and scalable optimization framework, FraudScan, that can adapt to emerging fraud patterns over time.Empirical analysis on two real-world datasets validates the effectiveness and efficiency of the proposed framework.

The "International Workshop on Recommender Systems for Citizens" (CitRec) is focused on a novel type of recommender systems both in terms of ownership and purpose: recommender systems run by citizens and serving society as a whole.

Our data analysis on real-world datasets shows that user preferences are intimately related with item categories, implying the non-negligible of category information for effective recommendation. Thus, in this paper, step by step we propose a unified item-category latent factor model by considering user-category, item-category and category-category interactions. Our approach can be applied to both the situations where an item belongs to either a single category (one-to-one) or multiple categories (one-to-many). Finally, empirical studies on the real-world datasets demonstrate the superiority of our approach in comparison with other counterparts.

In electronic commerce, buyers and sellers conduct transactions without physical interactions. In reputation systems, the trustworthiness of sellers is achieved by aggregating the ratings shared by other buyers with whom the sellers have ever conducted transactions. However, the ratings provided by buyers for evaluating the same seller could be diverse due to their different judgment criteria, which is referred as the subjectivity problem of reputation systems. It indicates that the ratings shared by some buyers may mislead other buyers with different personalities, making it challenging to aggregate the ratings properly in reputation systems. In this paper, in order to cope with the subjectivity problem, a hybrid architecture of reputation systems is proposed, which is based on coalition formation game theory. In the proposed module, buyers with the same subjectivity will automatically form a club, and share their ratings so as to build seller reputation within their club. The utility of a club is the profit created by the reputation system, which is further divided among the buyers of the club. Two utility allocation algorithms have been investigated, i.e., the proportional and Shapley allocations, respectively. Theoretical analysis and experimental results have shown that buyers with the same personality have the incentive to form a separate pure club if specific conditions are satisfied.

Although a previous paper shows that existing anti-malware tools (AMTs) may have high detection rate, the report is based on existing malware and thus it does not imply that AMTs can effectively deal with future malware. It is desirable to have an alternative way of auditing AMTs. In our previous paper, we use malware samples from android malware collection Genome to summarize a malware meta-model for modularizing the common attack behaviors and evasion techniques in reusable features. We then combine different features with an evolutionary algorithm, in which way we evolve malware for variants. Previous results have shown that the existing AMTs only exhibit detection rate of 20%-30% for 10 000 evolved malware variants. In this paper, based on the modularized attack features, we apply the dynamic code generation and loading techniques to produce malware, so that we can audit the AMTs at runtime. We implement our approach, named Mystique-S, as a service-oriented malware generation system. Mystique-S automatically selects attack features under various user scenarios and delivers the corresponding malicious payloads at runtime. Relying on dynamic code binding (via service) and loading (via reflection) techniques, Mystique-S enables dynamic execution of payloads on user devices at runtime. Experimental results on real-world devices show that existing AMTs are incapable of detecting most of our generated malware. Last, we propose the enhancements for existing AMTs.

As the number of vehicles grows rapidly each year, more and more traffic congestion occurs, becoming a big issue for civil engineers in almost all metropolitan cities. In this paper, we propose a novel pheromone-based traffic management framework for reducing traffic congestion, which unifies the strategies of both dynamic vehicle rerouting and traffic light control. Specifically, each vehicle, represented as an agent, deposits digital pheromones over its route, while roadside infrastructure agents collect the pheromones and fuse them to evaluate real-time traffic conditions as well as to predict expected road congestion levels in near future. Once road congestion is predicted, a proactive vehicle rerouting strategy based on global distance and local pheromone is employed to assign alternative routes to selected vehicles before they enter congested roads. In the meanwhile, traffic light control agents take online strategies to further alleviate traffic congestion levels. We propose and evaluate two traffic light control strategies, depending on whether or not to consider downstream traffic conditions. The unified pheromone-based traffic management framework is compared with seven other approaches in simulation environments. Experimental results show that the proposed framework outperforms other approaches in terms of traffic congestion levels and several other transportation metrics, such as air pollution and fuel consumption. Moreover, experiments over various compliance and penetration rates show the robustness of the proposed framework.

Web service composition is a process to compose homogenous or heterogeneous services together in order to create value-added services. Many non-functional features including QoS and user preferences have been adopted to guide such a process. However, two issues are observed: (1) the expressiveness of user preference is subject to quantitative preferences without proper use of qualitative preferences; (2) a highly preferred composite service may not be trustworthy, or a highly trustworthy composite service may not be preferable. The existing studies concentrate on either user preference or service trust, and fail to provide a systematic solution to integrate both user preference and service trust together for service compositions. To address these issues, we combine both qualitative and quantitative preferences as well as service trust together in the process of service composition. We investigate the application of heuristic algorithms on multi-objective optimization for the service composition problem. A new hybrid nature inspired intelligent algorithm is also proposed and compared with other popular heuristic algorithms. We aim to obtain optimal web service compositions that can satisfy these (potentially conflicting) constraints as much as possible. Results demonstrate the efficiency and effectiveness of our approach in comparison with other counterparts.

We address the collusive opinion fraud problem in online review portals, where groups of people work together to deliver deceptive reviews for manipulating the reputations of targeted items. Such collusive fraud is considered much harder to defend against, since the participants (or colluders) can evade detection by shaping their behaviors collectively so as not to appear suspicious. To alleviate this problem, countermeasures have been proposed that leverage the collective behaviors of colluders. The motivation stems from the observation that colluders typically act in a very synchronized way, as they are instructed by the same campaigns with common items to target and schedules to follow. However, the collective behaviors examined in existing solutions focus mostly on the external appearance of fraud campaigns, such as the campaign size and the size of the targeted item set. These signals may become ineffective once colluders have changed their behaviors collectively. Moreover, the detection algorithms used in existing approaches are designed to only make collusion inference on the input data; predictive models that can be deployed for detecting emerging fraud cannot be learned from the data. In this article, to complement existing studies on collusive opinion fraud characterization and detection, we explore more subtle behavioral trails in collusive fraud practice. In particular, a suite of homogeneity-based measures are proposed to capture the interrelationships among colluders within campaigns. Moreover, a novel statistical model is proposed to further characterize, recognize, and predict collusive fraud in online reviews. The proposed model is fully unsupervised and highly flexible to incorporate effective measures available for better modeling and prediction. Through experiments on two real-world datasets, we show that our method outperforms the state of the art in both characterization and detection abilities.

Transportation and mobility are central to sustainable urban development, where multiagent-based route guidance is widely applied. Traditional multiagent-based route guidance always seeks LET (least expected travel time) paths. However, drivers usually have specific expectations, i.e., tight or loose deadlines, which may not be all met by LET paths. We thus adopt and extend the probability tail model that aims to maximize the probability of reaching destinations before deadlines. Specifically, we propose a decentralized multiagent approach, where infrastructure agents locally collect intentions of concerned vehicle agents and formulate route guidance as a route assignment problem, to guarantee their arrival on time. Experimental results on real road networks justify its ability to increase the chance of arrival on time.

In agent-based simulation, emergent equilibrium describes the macroscopic steady states of agents' interactions. While the state of individual agents might be changing, the collective behavior pattern remains the same in macroscopic equilibrium states. Traditionally, these emergent equilibriums are calculated using Monte Carlo methods. However, these methods require thousands of repeated simulation runs, which are extremely time-consuming. In this paper, we propose a novel three-layer framework to efficiently compute emergent equilibriums. The framework consists of a macro-level pseudo-arclength equilibrium solver (PAES), a micro-level simulator (MLS) and a macro-micro bridge (MMB). It can adaptively explore parameter space and recursively compute equilibrium states using the predictor-corrector scheme. We apply the framework to the popular opinion dynamics and labour market models. The experimental results show that our framework outperformed Monte Carlo experiments in terms of computation efficiency while maintaining the accuracy.

In multiagent e-marketplaces, buying agents need to select good sellers by querying other buyers (called advisors). Partially Observable Markov Decision Processes (POMDPs) have shown to be an effective framework for optimally selecting sellers by selectively querying advisors. However, current solution methods do not scale to hundreds or even tens of agents operating in the e-market. In this paper, we propose the Mixture of POMDP Experts (MOPE) technique, which exploits the inherent structure of trust-based domains, such as the seller selection problem in e-markets, by aggregating the solutions of smaller sub-POMDPs. We propose a number of variants of the MOPE approach that we analyze theoretically and empirically. Experiments show that MOPE can scale up to a hundred agents thereby leveraging the presence of more advisors to significantly improve buyer satisfaction.

In trust systems, unfair rating attacks — where advisors provide ratings dishonestly — influence the accuracy of trust evaluation. A secure trust system should function properly under all possible unfair rating attacks; including dynamic attacks. In the literature, camouflage attacks are the most studied dynamic attacks. But an open question is whether more harmful dynamic attacks exist. We propose random processes to model and measure dynamic attacks. The harm of an attack is influenced by a user's ability to learn from the past. We consider three types of users: blind users, aware users, and general users. We found for all the three types, camouflage attacks are far from the most harmful. We identified the most harmful attacks, under which we found the ratings may still be useful to users.

In the arms race of attackers and defenders, the defense is usually more challenging than the attack due to the unpredicted vulnerabilities and newly emerging attacks every day. Currently, most of existing malware detection solutions are individually proposed to address certain types of attacks or certain evasion techniques. Thus, it is desired to conduct a systematic investigation and evaluation of anti-malware solutions and tools based on different attacks and evasion techniques. In this paper, we first propose a meta model for Android malware to capture the common attack features and evasion features in the malware. Based on this model, we develop a framework, MYSTIQUE, to automatically generate malware covering four attack features and two evasion features, by adopting the software product line engineering approach. With the help of MYSTIQUE, we conduct experiments to 1) understand Android malware and the associated attack features as well as evasion techniques; 2) evaluate and compare the 57 off-the-shelf anti-malware tools, 9 academic solutions and 4 App market vetting processes in terms of accuracy in detecting attack features and capability in addressing evasion. Last but not least, we provide a benchmark of Android malware with proper labeling of contained attack and evasion features.

Malware has posed a major threat to the Android ecosystem. Existing malware detection tools mainly rely on signature- or feature- based approaches, failing to provide detailed information beyond the mere detection. In this work, we propose a precise semantic model of Android malware based on Deterministic Symbolic Automaton (DSA) for the purpose of malware comprehension, detection and classification. It shows that DSA can capture the common malicious behaviors of a malware family, as well as the malware variants. Based on DSA, we develop an automatic analysis framework, named SMART, which learns DSA by detecting and summarizing semantic clones from malware families, and then extracts semantic features from the learned DSA to classify malware according to the attack patterns. We conduct the experiments in both malware benchmark and 223,170 real-world apps. The results show that SMART builds meaningful semantic models and outperforms both state-of-the-art approaches and anti-virus tools in malware detection. SMART identifies 4583 new malware in real-world apps that are missed by most anti-virus tools. The classification step further identifies new malware variants and unknown families.

Existing feature-based recommendation methods incorporate auxiliary features about users and/or items to address data sparsity and cold start issues. They mainly consider features that are organized in a flat structure, where features are independent and in a same level. However, auxiliary features are often organized in rich knowledge structures (e.g. hierarchy) to describe their relationships. In this paper, we propose a novel matrix factorization framework with recursive regularization -- ReMF, which jointly models and learns the influence of hierarchically-organized features on user-item interactions, thus to improve recommendation accuracy. It also provides characterization of how different features in the hierarchy co-influence the modeling of user-item interactions. Empirical results on real-world data sets demonstrate that ReMF consistently outperforms state-of-the-art feature-based recommendation methods.

In e-commerce systems, user preference can be inferred from multivariate implicit feedback (i.e., actions). However, most methods merely focus on homogeneous implicit feedback (i.e., purchase). In this paper, we adopt another two typical actions, i.e., view and like, as auxiliaries to enhance purchase recommendation, whereby a trinity Bayesian personalized ranking (TBPR) method is proposed. Specifically, we introduce trinity preference to investigate the difference of users' preference among three types of items: 1) items with purchase action; 2) items with only auxiliary actions; 3) items without any action. Empirical study on the real-world dataset demonstrates that our method significantly outperforms state-of-the-art algorithms.

Although flat item category structure where categories are independent in a same level has been well studied to enhance recommendation performance, in many real applications, item category is often organized in hierarchies to reflect the inherent correlations among categories. In this paper, we propose a novel matrix factorization model by exploiting category hierarchy from the perspectives of users and items for effective recommendation. Specifically, a user (an item) can be influenced (characterized) by her preferred categories (the categories it belongs to) in the hierarchy. We incorporate how different categories in the hierarchy co-influence a user and an item. Empirical results show the superiority of our approach against other counterparts.

Social commitment protocols regulate interactions of agents in multiagent systems. Several methods have been developed to analyze properties of commitment protocols. However, analysis of an agent's behavior in a commitment protocol, which should take into account the agent's goals and beliefs, has received less attention. In this paper we present ProMoca framework to address this issue. Firstly, we develop an expressive formal language to model agents with respect to their commitments. Our language provides dedicated elements to define commitment protocols, and model agents in terms of their goals, behaviors, and beliefs. Furthermore, our language provides probabilistic and non-deterministic elements to model uncertainty in agents' beliefs. Secondly, we identify two essential properties of an agent with respect to a commitment protocol, namely compliance and goal satisfaction. We formalize these properties using a probabilistic variant of linear temporal logic. Thirdly, we adapt a probabilistic model checking algorithm to automatically analyze compliance and goal satisfaction properties. Finally, we present empirical results about efficiency and scalability of ProMoca.

Automated negotiations play an important role in various domains modeled as multiagent systems, where agents represent human users and adopt different negotiation strategies. Generally, given a multiagent system, a negotiation strategy should be robust in the sense that most agents in the system have the incentive to choose it rather than other strategies. Empirical game-theoretic (EGT) analysis is a game-theoretic analysis approach to investigate the robustness of different strategies based on a set of empirical results. In this study, we propose that model-checking techniques can be adopted to improve EGT analysis for negotiation strategies. The dynamics of strategy profiles can be modeled as a labeled transition system using the counter abstraction technique. We define single-agent best deviation to represent the strategy deviations during negotiation, which focuses on each agent's best deviation benefit and is different from best single-agent deviation used in previous work. Two interesting properties in EGT analysis, i.e., empirical pure strategy Nash equilibrium and best reply cycle, are automatically verified to investigate the robustness of different strategies. For demonstration, the top-six strategies from the automated negotiating agents competition 2010-2012 are studied in terms of their robustness performance. In addition to identifying the most robust strategies, we supply complete rankings among them in different settings. We show that model checking is applicable and efficient to perform robustness analysis of negotiation strategies.

This paper aims at solving the stochastic shortest path problem in vehicle routing, the objective of which is to determine an optimal path that maximizes the probability of arriving at the destination before a given deadline. To solve this problem, we propose a data-driven approach, which directly explores the big data generated in traffic. Specifically, we first reformulate the original shortest path problem as a cardinality minimization problem directly based on samples of travel time on each road link, which can be obtained from the GPS trajectory of vehicles. Then, we apply an ℓ1-norm minimization technique and its variants to solve the cardinality problem. Finally, we transform this problem into a mixed-integer linear programming problem, which can be solved using standard solvers. The proposed approach has three advantages over traditional methods. First, it can handle various or even unknown travel time probability distributions, while traditional stochastic routing methods can only work on specified probability distributions. Second, it does not rely on the assumption that travel time on different road segments is independent of each other, which is usually the case in traditional stochastic routing methods. Third, unlike other existing methods which require that deadlines must be larger than certain values, the proposed approach supports more flexible deadlines. We further analyze the influence of important parameters to the performances, i.e., accuracy and time complexity. Finally, we implement the proposed approach and evaluate its performance based on a real road network of Munich city. With real traffic data, the results show that it outperforms traditional methods.

We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques.

User-based collaborative filtering , a widely used nearest neighbour-based recommendation technique, predicts an item’s rating by aggregating its ratings from similar users. User similarity is traditionally calculated by cosine similarity or the Pearson correlation coefficient . However, both of these measures consider only the direction of rating vectors, and suffer from a range of drawbacks. To overcome these issues, we propose a novel Bayesian similarity measure based on the Dirichlet distribution, taking into consideration both the direction and length of rating vectors. We posit that not all the rating pairs should be equally counted in order to accurately model user correlation. Three different evidence factors are designed to compute the weights of rating pairs. Further, our principled method reduces correlation due to chance and potential system bias. Experimental results on six real-world datasets show that our method achieves superior accuracy in comparison with counterparts.

Reputation systems aggregate the ratings provided by buyers to gauge the reliability of sellers in e-marketplaces. The evaluation accuracy of seller reputation significantly impacts the sellers' future utility. The existence of unfair ratings is well-recognized to negatively affect the accuracy of reputation evaluation. Most of the existing approaches dealing with unfair ratings focus on filtering/discounting/aligning the possible unfair ratings caused by malicious attacks or subjective difference. However, these approaches are not effective against unfair ratings in the form of misreporting (e.g., a well-behaving buyer misjudged a seller and provided a negative rating to a transaction which deserves a positive one, and the buyer is willing to revert the misreported negative rating). In this case, how should the buyer undo the damage caused by such misreported ratings and help the seller recover utility loss? In this paper, we propose a reputation revision mechanism to mitigate the negative effects of the misreported ratings. The proposed mechanism temporarily inflates the reputation of the misjudged seller for a period of time, which allows the seller to recover his utility loss caused by the misreported ratings. Extensive realistic simulation based experiments demonstrate the necessity and effectiveness of the proposed mechanism.

Commitment protocols provide an effective formalism for the regulation of agent interaction. Although existing work mainly focus on the design-time development of static commitment protocols, recent studies propose methods to create them dynamically at run-time with respect to the goals of the agents. These methods require agents to verify new commitment protocols taking their goals, and beliefs about the other agents’ behavior into account. Accordingly, in this paper, we first propose a probabilistic model to formally capture commitment protocols according to agents’ beliefs. Secondly, we identify a set of important properties for the verification of a new commitment protocol from an agent’s perspective and formalize these properties in our model. Thirdly, we develop probabilistic model checking algorithms with advanced reduction for efficient verification of these properties. Finally, we implement these algorithms as a tool and evaluate the proposed properties over different commitment protocols.

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

Online review fraud has evolved in sophistication by launching intelligent campaigns where a group of coordinated participants work together to deliver deceptive reviews for the designated targets. Such collusive fraud is considered much harder to defend against as these campaign participants are capable of evading detection by shaping their behaviors collectively so as not to appear suspicious. The present work complements existing studies by exploring more subtle behavioral trails connected with collusive review fraud. A novel statistical model is proposed to further characterize, recognize, and forecast collusive fraud in online reviews. The proposed model is completely unsupervised, which bypasses the difficulty of manual annotation required for supervised modeling. It is also highly flexible to incorporate collusion characteristics available for better modeling and prediction. Experiments on two real-world datasets demonstrate the effectiveness of the proposed method and the improvements in learning and predictive abilities.

Web service composition is a process to compose homogenous or heterogeneous services together in order to create value-added services. Many non-functional features including QoS and user preferences have been adopted to guide such a process. However, two issues are observed: (1) the expressiveness of user preference is subject to quantitative preferences without proper use of qualitative preferences, (2) a highly preferred composite service may not be trustworthy, or a highly trustworthy composite service may not be preferable. To address these issues, we combine both qualitative and quantitative preferences as well as service trust together in the process of service composition. We aim to obtain optimal web service compositions that can satisfy these (potentially conflicting) constraints as much as possible. Experimental results demonstrate the efficiency and effectiveness of our approach in comparison with other counterparts.

Spam campaigns spotted in popular product review websites (e.g., amazon.com) have attracted mounting attention from both industry and academia, where a group of online posters are hired to collaboratively craft deceptive reviews for some target products. The goal is to manipulate perceived reputations of the targets for their best interests. Many efforts have been made to detect such colluders by extracting pointwise features from individual reviewers/reviewer-groups, however, pairwise features which can potentially capture the underlying correlations among colluders are either ignored or just explored insufficiently in the literature. We observed that pairwise features can be more robust to model the relationships among colluders since they, as the ingredients of spam campaigns, are correlated in nature. In this paper, we explore multiple heterogeneous pairwise features in virtue of some collusion signals found in reviewers' rating behaviors and linguistic patterns. In addition, an unsupervised and intuitive colluder detecting framework has been proposed which can benefit from these pairwise features. Extensive experiments on real dataset show the effectiveness of our method and satisfactory superiority over several competitors.

The performance of trust models highly depend on the characteristics of the environments where they are applied. Thus, it becomes challenging to choose a suitable trust model for a given e-marketplace environment, especially when ground truth about the agent (buyer and seller) behavior is unknown (called unknown environment). We propose a case-based reasoning framework to choose suitable trust models for unknown environments, based on the intuition that if a trust model performs well in one environment, it will do so in another similar environment. Firstly, we build a case base with a number of simulated environments (with known ground truth) along with the trust models most suitable for each of them. Given an unknown environment, case-based retrieval algorithms retrieve the most similar case(s), and the trust model of the most similar case(s) is chosen as the most suitable model for the unknown environment. Evaluation results confirm the effectiveness of our framework in choosing suitable trust models for different e-marketplace environments.

To find diversified solutions converging to true Pareto fronts (PFs), hypervolume (HV) indicator-based algorithms have been established as effective approaches in multiobjective evolutionary algorithms (MOEAs). However, the bottleneck of HV indicator-based MOEAs is the high time complexity for measuring the exact HV contributions of different solutions. To cope with this problem, in this paper, a simple and fast hypervolume indicator-based MOEA (FV-MOEA) is proposed to quickly update the exact HV contributions of different solutions. The core idea of FV-MOEA is that the HV contribution of a solution is only associated with partial solutions rather than the whole solution set. Thus, the time cost of FV-MOEA can be greatly reduced by deleting irrelevant solutions. Experimental studies on 44 benchmark multiobjective optimization problems with 2-5 objectives in platform jMetal demonstrate that FV-MOEA not only reports higher hypervolumes than the five classical MOEAs (nondominated sorting genetic algorithm II (NSGAII), strength Pareto evolutionary algorithm 2 (SPEA2), multiobjective evolutionary algorithm based on decomposition (MOEA/D), indicator-based evolutionary algorithm, and S-metric selection based evolutionary multiobjective optimization algorithm (SMS-EMOA)), but also obtains significant speedup compared to other HV indicator-based MOEAs.

Watchdog technique is a fundamental building block to many trust systems that are designed for securing wireless sensor networks (WSNs). Unfortunately, this kind of technique consumes much energy and hence largely limits the lifespan of WSN. Although the state-of-the-art studies have realized the importance of trust systems' efficiency in WSNs and proposed several preliminary solutions, they have overlooked to optimize the watchdog technique, which is perhaps among the top energy-consuming units. In this paper, we reveal the inefficient use of watchdog technique in existing trust systems, and thereby propose a suite of optimization methods to minimize the energy cost of watchdog usage, while keeping the system's security in a sufficient level. Our contributions consist of theoretical analyses and practical algorithms, which can efficiently and effectively schedule the watchdog tasks depending on the sensor nodes' locations and the target nodes' trustworthiness. We have evaluated our algorithms through experiments on top of a WSNET simulation platform and an in-door WSN testbed in our collaborative lab. The results have successfully confirmed that our watchdog optimization techniques can save at least 39.44% energy without sacrificing much security (<;0.06 in terms of trust accuracy and robustness), even in some cases enhance the protection against certain attacks.

No abstract available.

Although users' preference is semantically reflected in the free-form review texts, this wealth of information was not fully exploited for learning recommender models. Specifically, almost all existing recommendation algorithms only exploit rating scores in order to find users' preference, but ignore the review texts accompanied with rating information. In this paper, we propose a novel matrix factorization model (called TopicMF) which simultaneously considers the ratings and accompanied review texts. Experimental results on 22 real-world datasets show the superiority of our model over the state-of-the-art models, demonstrating its effectiveness for recommendation tasks.

Trust has been used to replace or complement rating-based similarity in recommender systems, to improve the accuracy of rating prediction. However, people trusting each other may not always share similar preferences. In this paper, we try to fill in this gap by decomposing the original single-aspect trust information into four general trust aspects, i.e. benevolence, integrity, competence, and predictability, and further employing the support vector regression technique to incorporate them into the probabilistic matrix factorization model for rating prediction in recommender systems. Experimental results on four datasets demonstrate the superiority of our method over the state-of-the-art approaches.

Reputation models depend on the ratings provided by buyers togauge the reliability of sellers in multi-agent based e-commerce environment. However, there is no prevention forthe cases in which a buyer misjudges a seller, and provides a negative rating to an original satisfactory transaction. In this case,how should the seller get his reputation repaired andutility loss recovered? In this work, we propose a mechanism to mitigate the negativeeffect of the misreported ratings. It temporarily inflates the reputation of thevictim seller with a certain value for a period of time. This allows the seller to recover hisutility loss due to lost opportunities caused by the misreported ratings. Experiments demonstrate the necessity and effectiveness of the proposed mechanism.

Truthful bidding is a desirable property for continuous double auctions (CDAs). Many incentive mechanisms have been proposed to elicit truthful bids. However, existing truthful CDA mechanisms often overlook the possibility that sellers may choose not to deliver the auctioned items to buyers as promised. In this situation, buyers may become unwilling to bid their true valuations in the future to compensate for their risks of being cheated, thereby rendering CDAs ineffective. In this paper, we propose a novel reputation-aware CDA (named RCDA) mechanism to consider the honesty of auction participants. It dynamically adjusts bids and asks according to the reputation of participants to reflect the risks involved in the transactions. Theoretical analysis proves that RCDA is effective in eliciting truthful bids from buyers and sellers in the presence of possible dishonest behavior from both buyers and sellers.

Trust is one source of information that has been widely adopted to personalize online services for users, such as in product recommendations. However, trust information is usually very sparse or unavailable for most online systems. To narrow this gap, we propose a principled approach that predicts implicit trust from users' interactions, by extending a well-known trust antecedents framework. Specifically, we consider both local and global trustworthiness of target users, and form a personalized trust metric by further taking into account the active user's propensity to trust. Experimental results on two real-world datasets show that our approach works better than contemporary counterparts in terms of trust ranking performance when direct user interactions are limited.

There is a need for robust and effective trust management. Different security problems result in different requirements to the design of trust management, and the existing attacks in trust management for security are yet to be solved. In this paper, we first propose a framework to classify desired properties of trust management for each type of security problems. We then investigate typical representative attacks and existing solutions in trust management for security. By considering both these security properties and attacks on trust management systems, our work serves to propel the design of more effective and robust trust management systems for security.

Reputation systems have contributed much to the success of electronic marketplaces. However, the problem of unfair testimonies has to be addressed effectively to improve the robustness of reputation systems. Until now, most of the existing approaches focus only on reputation systems using binary testimonies, and thus have limited applicability and effectiveness. In this paper, We propose an integrated CLUstering‐Based approach called iCLUB to filter unfair testimonies for reputation systems using multinominal testimonies, in an example application of multiagent‐based e‐commerce. It adopts clustering techniques and considers buyer agents’ local as well as global knowledge about seller agents. Experimental evaluation demonstrates the promising results of our approach in filtering various types of unfair testimonies, its robustness against collusion attacks, and better performance compared to competing models.

Abstract In this article, we propose a novel super‐agent‐based framework for reputation management and community formation in decentralized systems. We describe this framework in the context of Web service selection where agents with more capabilities act as super‐agents. These super‐agents serve as reputation managers to maintain reputation information of services and share the information with other consumer agents that have fewer capabilities than the super‐agents. In addition, super‐agents can maintain communities and build community‐based reputation for a service based on the opinions from all community members that have similar interests and judgement criteria as the super‐agents or the other community members. A practical reward mechanism is also introduced to create incentives for super‐agents to contribute their resources (to maintain reputation and form communities) and provide truthful reputation information. Experimental results obtained through simulation confirm that our approach achieves better effectiveness and scalability compared to the systems that do not use super‐agents and that do not form communities.

An important consideration of multiobjective optimization (MOO) is the quantitative metrics used for defining the optimality of different solution sets, which is also the basic principle for the design and evaluation of MOO algorithms. Although a plethora of performance metrics have been proposed in the MOO context, there has been a lack of insights on the relationships between metrics. In this paper, we first group the major MOO metrics proposed to date according to four core performance criteria considered in the literature, namely, capacity, convergence, diversity, and convergence-diversity. Then, a comprehensive study is conducted to investigate the relationships among representative group metrics, including generational distance, ϵ-indicator (I(1)ϵ+), spread (∆), generalized spread (∆∗), inverted generational distance, and hypervolume. Experimental results indicated that these six metrics show high consistencies when Pareto fronts (PFs) are convex, whereas they show certain contradictions on concave PFs.

As the rapid development of China's e-commerce in recent years and the underlying evolution of adversarial spamming tactics, more sophisticated spamming activities may carry out in Chinese review websites. Empirical analysis, on recently crawled product reviews from a popular Chinese e-commerce website, reveals the failure of many state-of-the-art spam indicators on detecting collusive spammers. Two novel methods are then proposed: 1) a KNN-based method that considers the pairwise similarity of two reviewers based on their group-level relational information and selects k most similar reviewers for voting; 2) a more general graph-based classification method that jointly classifies a set of reviewers based on their pairwise transaction correlations. Experimental results show that both our methods promisingly outperform the indicator-only classifiers in various settings.

In this paper we produce an algorithm for presenting messages to users in social networks that integrates reasoning about the message, the author, the recipient and the social network. Our proposed model was derived on the basis of immersion within three different existing social networking environments, that of Courser a, Reddit, and medical self-help groups such as PatientsLikeMe. We first present three models, each of which is designed to perform well within the context of one specific social network. From here we derive a generalized model which can be effective regardless of social network context. We conclude with a discussion of possible directions for future research, with an emphasis on promoting the use of trust modeling and user modeling, in a view to exploring additional networks and include as well a comparison to competing models within the artificial intelligence literature. Our aim is to offer insights into coping with the massive amount of information that currently resides within our social networks.

Lack of motivation to provide ratings and eligibility to rate generally only after purchase restrain the effectiveness of recommender systems and contribute to the well-known data sparsity and cold start problems. This paper proposes a new information source for recommender systems, called prior ratings. Prior ratings are based on users' experiences of virtual products in a mediated environment, and they can be submitted prior to purchase. A conceptual model of prior ratings is proposed, integrating the environmental factor presence whose effects on product evaluation have not been studied previously. A user study conducted in website and virtual store modalities demonstrates the validity of the conceptual model, in that users are more willing and confident to provide prior ratings in virtual environments.

ABSTRACT In this paper, we present a trust‐modeling framework for message propagation and evaluation in vehicular ad hoc networks. In the framework, peers share information regarding road condition or safety, and others provide opinions about whether the information can be trusted. More specifically, our trust‐based message propagation model collects and propagates peers' opinions in an efficient, secure, and scalable way by dynamically controlling information dissemination. The trust‐based message evaluation model allows peers to derive a local action decision about whether to follow the information by evaluating the information in a distributed and collaborative fashion while taking into account others' opinions. Experimental results demonstrate that our proposed trust‐modeling framework promotes network scalability and system effectiveness, which are the two essentially important factors for the popularization of vehicular ad hoc networks, in information propagation and evaluation under the pervasive presence of false information. In particular, we clarify how our relay control serves to decrease the number of inappropriate actions taken on the basis of malicious information and enables honest peers to produce a greater number of deliveries within the network. Copyright © 2012 John Wiley & Sons, Ltd.

In this article, we present a framework of use in electronic marketplaces that allows buying agents to model the trustworthiness of selling agents in an effective way, making use of seller ratings provided by other buying agents known as advisors. The trustworthiness of the advisors is also modeled, using an approach that combines both personal and public knowledge and allows the relative weighting to be adjusted over time. Through a series of experiments that simulate e-marketplaces, including ones where sellers may vary their behavior over time, we are able to demonstrate that our proposed framework delivers effective seller recommendations to buyers, resulting in important buyer profit. We also propose limiting seller bids as a method for promoting seller honesty, thus facilitating successful selection of sellers by buyers, and demonstrate the value of this approach through experimental results. Overall, this research is focused on the technological aspects of electronic commerce and specifically on technology that would be used to manage trust.

Service selection is one of the important problems in applications of multi-agent systems. A qualitative way is desirable for service selection according to agents' preferences on non-functional Quality of Service (QoS) attributes of services. Howeve

In e-marketplaces with limited inventory where buyers' demand is larger than sellers' supply, promoting honesty raises new challenges: sellers may behave dishonestly because they can sell out all products without the necessity of gaining high reputation; buyers may provide untruthful ratings to mislead other buyers in order to have a higher chance to obtain the limited products. In this paper, we propose a novel incentive mechanism to promote honesty in such e-marketplaces. More specifically, our mechanism models both buyer and seller honesty. It offers higher prices to the products provided by honest sellers so that the sellers can gain more profit. Honest buyers also have a higher chance to do business with honest sellers and are able to gain more utility. Theoretical analysis and experimental results show that our mechanism promotes both buyer and seller honesty. Finally, we address the re-entry problem by imposing membership fees on new sellers. We show that the membership fee can discourage sellers from re-entry both in theoretical analysis and experimental validation.

Stereotypical trust modeling can be adopted by a buyer to effectively evaluate trustworthiness of a seller who has little or no past experience in e-marketplaces. The buyer forms trust stereotypes based on her past experience with other sellers. However, when the buyer has limited past experience with sellers, the formed stereotypes cannot accurately reflect her trust evaluation towards sellers. To address this issue, we propose a novel generalized stereotypical trust model. Specifically, we first build a semantic ontology to represent hierarchical relationships among seller attribute values. We then propose a fuzzy semantic decision tree (FSDT) learning method to construct trust stereotypes that generalizes over seller non-nominal attributes by splitting their values in a fuzzy manner, and generalizes over nominal attributes by replacing their specific values with more general terms according to the ontology. Experimental results confirm that our proposed model can more accurately measure the trustworthiness of sellers in simulated e-marketplaces where buyers have limited experience with sellers.

A reputation system is a common approach to induce sellers' good conduct in an e-marketplace, where feedback about a seller will be used by a potential future buyer to decide whether to conduct a transaction with the seller. However, reputation systems suffer from some inherent problems. We propose a novel approach that changes the fundamental role of feedback - the impact of feedback is exerted only to the transaction that the feedback is obtained from, but not future transactions. Consequently, some inherent problems of reputation systems no longer apply in our scheme. Our approach thus offers an alternative to traditional reputation systems, to induce sellers' good conduct.

Reputation systems deployed in popular online auction sites simply aggregate feedback about a seller's past transactions. By studying a real auction site dataset, we infer that a non-negligible fraction of unsatisfactory transactions involve sellers with high reputation. Such a phenomenon can be interpreted by motivation theory from behaviorial science: A seller with high reputation has more business opportunities. Bad feedback for latest transactions do not immediately affect his reputation adequately to hurt business, hence he may not be as prudent as before. In this work, we propose the concept of imprudence to study and detect the inappropriate behavior of a 'reliable' seller (i.e., the one with high reputation computed using conventional approaches). Specifically, we first identify and verify the features that influence a seller's imprudence behavior. We then design a novel intelligent buying agent to combine these factors using logistic regression for predicting and studying the probability of imprudence of a target seller. We validate our approach using real datasets driven experiments.

In a reputation system for multiagent based electronic marketplaces where the number of high quality products provided by good selling agents is unlimited, buying agents often share seller information without the need to consider possible utility loss. However, when those good sellers have limited inventory, buyers may have to be concerned about the possibility of losing the opportunity to do business with the good sellers if the buyers provide truthful information about sellers, due to the competition from other buyers. In this paper, we propose an adaptive mechanism built on a game theoretic basis for buyers to determine their optimal reputation reporting strategy, by modeling both the competency and willingness of other buyers in reporting seller reputation and strategically choosing reporting behaviours that maximize their utility according to the modeling results. The results of the experiments carried out in a simulated competitive e-marketplace confirm that our proposed mechanism leads to better utility for buyers in such an environment.

Reputation systems are highly prone to unfair rating attacks. Though many approaches for detecting unfair ratings have been proposed so far, their performance is often affected by the environment where they are applied. For a given unknown real environment, it is difficult to choose the most suitable approach for detecting unfair ratings as the ground truth data necessary to evaluate the accuracy of the detection approaches remains unknown. In this paper, we propose a novel Context-AwaRE (CARE) framework, to choose the most suitable unfair rating detection approach for a given unknown real environment. The framework first identifies simulated environments, closely similar to that of the unknown environment. The detection approaches performing well in the most similar simulated environments are then chosen as the suitable ones for the unknown real environment. Detailed experiments illustrate that the CARE framework can choose the most suitable detection approach to accurately distinguish fair and unfair ratings for any given unknown environment.

Similarity measurement is essential in many preference-based personalized applications such as collaborative recommendation and service selection. Up to date, current researches have mainly focused on the measurements for quantitative preference rather than qualitative preference, although the latter has attracted much attention recently. Only a very few methods to measure user similarity are proposed. This paper aims to fill in this gap by proposing an intuitive similarity measurement for conditional qualitative preference which is represented by CP-nets. Experimental results based on two expanded real-life datasets demonstrate that our similarity measurement is not only able to correctly reflect user's preference changes, but also effective to identify similar users.

In this paper, we propose a novel incentive mechanism for promoting honesty in electronic marketplaces that is based on trust modeling. In our mechanism, buyers model other buyers and select the most trustworthy ones as their neighbors to form a social network which can be used to ask advice about sellers. In addition, however, sellers model the reputation of buyers based on the social network. Reputable buyers provide truthful ratings for sellers, and are likely to be neighbors of many other buyers. Sellers will provide more attractive products to reputable buyer to build their own reputation. We theoretically prove that a marketplace operating with our mechanism leads to greater profit both for honest buyers and honest sellers. We emphasize the value of our approach through a series of illustrative examples and in direct contrast to other frameworks for addressing agent trustworthiness. In all, we offer an effective approach for the design of e‐marketplaces that is attractive to users, through its promotion of honesty.

An effective Collaborative Intrusion Detection Network (CIDN) allows distributed Intrusion Detection Systems (IDSes) to collaborate and share their knowledge and opinions about intrusions, to enhance the overall accuracy of intrusion assessment as well as the ability of detecting new classes of intrusions. Toward this goal, we propose a distributed Host-based IDS (HIDS) collaboration system, particularly focusing on acquaintance management where each HIDS selects and maintains a list of collaborators from which they can consult about intrusions. Specifically, each HIDS evaluates both the false positive (FP) rate and false negative (FN) rate of its neighboring HIDSes' opinions about intrusions using Bayesian learning, and aggregates these opinions using a Bayesian decision model. Our dynamic acquaintance management algorithm allows each HIDS to effectively select a set of collaborators. We evaluate our system based on a simulated collaborative HIDS network. The experimental results demonstrate the convergence, stability, robustness, and incentive-compatibility of our system.

Electronic marketplaces are not always easily regulated using traditional legal systems. As a result, suitable dispute prevention and resolution mechanisms for electronic marketplaces are of interest to design. In this paper, we design a multiagent-based e-marketplace where participants represented by autonomous software agents may be egocentric, strategic and even malicious. Our marketplace focuses on trading arbitrable and replicatable services, such as computational resources and data storage, over the Internet. We propose a novel dispute prevention and resolution mechanism that is theoretically proven to be able to induce good conduct for marketplace participants. Our marketplace also features cost-effectiveness, robustness and budget balance.

Online reviews, as one kind of new marketing methods, are becoming increasingly important in influencing preferences and decision-making of consumers. In view of this, this paper conducts an empirical study on Dangdang Books, constructs a research framework on correlation between product sales and aspects of online reviews, and explores the correction through the panel data econometric model. Compared to former research, our work analyzes real data on product sales instead of the rank of sales. Based on our study results and comparison with others' research results on Amazon (amazon.com), we provide several guiding suggestions for online review systems in the Chinese context. These systems should pay attention to emotional tendency of reviews, reviewer ranking mechanisms, spotlight reviews, review format and review voting mechanisms.

There is an urgent need of effective trust management for vehicular ad-hoc networks (VANETs), given the dire consequences of acting on false information sent out by malicious peers in this context. In this paper, we first discuss the challenges for trust management caused by the important characteristics of VANET environments. We then survey existing trust models in multi-agent systems, mobile ad-hoc networks (MANETs) and VANETs, and point out their key issues. Based on these studies, we suggest desired properties towards effective trust management in VANETs, setting up clear goals for researchers in this area.

In this paper, we promote a model for peer-based intelligent tutoring that leverages the past learning experiences of students with a repository of learning objects. Consistent with McCalla’s ecological approach, we determine appropriate peers and appropriate learning objects to direct a new student's learning. In particular, we focus on allowing peers to provide annotations of learning objects. We revisit a procedure developed to select which annotations to present to students in order to improve their learning: one that combines a modeling of the reputation of the annotation (based on its approval or disapproval by previous students), the reputability of the annotator (based on the reputation of all annotations left by the student) and the similarity of the raters with the new student. Our focus is on developing effective validation of the procedure’s benefit, using an approach of simulated student learning. This is achieved by developing algorithms in greater detail and then making particular design decisions for the simulation in order to manage the reputability of the annotators and annotations in a way that enables the best learning objects to be employed for the tutoring. We are able to demonstrate the value of our proposed approach using distinct measures of rater similarity. We conclude with a comparison to related work and a view to future directions for the research. As a result, we present an approach for interpreting data from interactions with previous students in order to influence how to interact with current and future students, to enable effective learning.

E-learning systems are now considered a core IT support in most Institute of Higher Learning. Each student is assigned a set of courses based on their preferences for each semester. Learning materials are posted and evaluated through the system. Mining access and usage log data of the e-learning system can give insights such as on how these materials are accessed by students in their order of preferences on each of the courses. A study was carried out, and the results show that there is good correlation between students’ performance and e-learning usage. In this paper, we describe our methodology on analyzing students’ usage of e-learning systems for course management with the help of cloud computing services. We present a student performance predictive model built from the retrieved logs that is confirmed to achieve sufficiently good accuracy.

Incomplete preferences of agents may render service selection ineffective. We address this problem by proposing a set of collaborative approaches to complementing agents' incomplete preferences in a qualitative way. For an agent, the approaches first find its similar agents, and then base the similar agents' qualitative preferences to complement this agent's missing preferences. We analyze and compare these approaches and provide experimental results to justify our arguments. We also compare our approach with the classic collaborative filtering and show the competitive advantages of our approach in service selection. Our work thus serves as an important step towards effective service selection.

This paper provides an approach for improving the trust modelling of users when a social network of advisors is employed (for example when advisors are recommending the most trustworthy service providers). We present three important improvements to trust modelling, two directly relating to the size of the network (through either the use of a threshold or by setting a maximum network size) and a third (advisor referrals) which focuses on ensuring that the advisors have attained an appropriate level of expertise, for the advice that they provide. Experimental results confirm the value of our methods when choosing parameters in a principled manner, leading to improvements in the accuracy of trust modelling (shown in the context of electronic marketplaces). In all, this research provides insights into how to set the size and composition of social networks, in order to effectively integrate the advice of peers when modelling the trust of users.

In this paper, we propose a reputation mechanism for virtual marketplaces. The proposed approach is based on five-sense oriented feedback provision with the support of existing virtual reality technologies. We have conducted user studies to analyse users' attitude towards this new approach. These studies reveal that users prefer virtual marketplaces with our proposed reputation mechanism over that with traditional reputation mechanisms, and that our mechanism can effectively ensure user's trust in the virtual marketplaces and simultaneously promote user's trust in other users. Our approach is based on feedback from other users. Feedback from users could be very subjective and misleading for other users. Hence, we propose a novel mechanism to align subjective user feedback before reputation computations in virtual marketplaces. Results of our experiment demonstrate that with our feedback alignment approach, buyers can more accurately model sellers' reputation.

Social networks are fundamental to virtual communities (e.g., forums, blogs) and virtual communities benefit from well-established social networks. As making friends with other members is a common way to establish social relationships and people need to decide whom they should trust when making friends, friend recommendation has received considerable attention in virtual communities. Towards this goal, we first formulate hypotheses on factors that influence trust and the probability of establishing friendships from various interaction attributes in virtual communities. Through experiments on real interaction and friendship data, we validate the proposed hypotheses and propose a novel interaction ranking-based trustworthy friend recommendation model called IntRank for recommending trustworthy friends to community members. Different from traditional friend recommendation mechanisms, IntRank is built on the foundation of carefully verified interaction attributes that influence trust and friendship probability in virtual communities. It is able to effectively recommend trustworthy friends as confirmed by the performance evaluation results.

PGP Web of Trust where users can sign digital signatures on public key certificates of other users has been successfully applied in securing emails and files transmitted over the Internet. However, its rigorous restrictions on utilizable trust relationships and acceptable signatures limit its performance. In this paper, we first make some modification and extension to PGP Web of Trust by relaxing those constraints. In addition, we propose a novel method to further expand trusted neighborhood of users by merging the signatures of the trusted neighbors and finding the similar users based on the merged signature set. Confirmed by the experiments carried out in different simulated real-life scenarios, our method applied to both the modified and extended PGP methods can improve their performance. With the expansion of trusted neighborhood, the performance of the original PGP Web of Trust is also improved considerably.

The accuracy of detecting intrusions within a Collaborative Intrusion Detection Network (CIDN) depends on the efficiency of collaboration between peer Intrusion Detection Systems (IDSes) as well as the security itself of the CIDN. In this paper, we propose Dirichlet-based trust management to measure the level of trust among IDSes according to their mutual experience. An acquaintance management algorithm is also proposed to allow each IDS to manage its acquaintances according to their trustworthiness. Our approach achieves strong scalability properties and is robust against common insider threats, resulting in an effective CIDN. We evaluate our approach based on a simulated CIDN, demonstrating its improved robustness, efficiency and scalability for collaborative intrusion detection in comparison with other existing models.

An increasingly large number of cars are being equipped with global positioning system and Wi-Fi devices, enabling vehicle-to-vehicle (V2V) communication with the goal of providing increased passenger and road safety. This technology actuates the need for agents that assist users by intelligently processing the received information. Some of these agents might become self-interested and try to maximize car owners' utility by sending out false information. Given the dire consequences of acting on false information in this context, there is a serious need to establish trust among agents. The main goal of this paper is then to develop a framework that models the trustworthiness of the agents of other vehicles, in order to receive the most effective information. We develop a multifaceted trust modeling approach that incorporates role-, experience-, priority-, and majority-based trust and this is able to restrict the number of reports that are received. We include an algorithm that proposes how to integrate these various dimensions of trust, along with experimentation to validate the benefit of our approach, emphasizing the importance of each of the different facets that are included. The result is an important methodology to enable effective V2V communication via intelligent agents.

An effective Collaborative Intrusion Detection Network (CIDN) allows distributed Intrusion Detection Systems (IDSes) to collaborate and share their knowledge and opinions about intrusions, to enhance the overall accuracy of intrusion assessment as well as the ability of detecting new classes of intrusions. Towards this goal, we propose a distributed Host-based IDS (HIDS) collaboration system, particularly focusing on acquaintance management where each HIDS selects and maintains a list of collaborators from which they can consult about intrusions. More specifically, each HIDS evaluates both the false positive (FP) rate and false negative (FN) rate of its neighboring HIDSes' opinions about intrusions using Bayesian learning, and aggregates their opinions about intrusions using a Bayesian decision model. Our dynamic acquaintance management algorithm allows each HIDS to effectively select a set of collaborators. We evaluate our system based on a simulated collaborative HIDS network. The experimental results demonstrate the convergence, stability and incentive of our system.

In this paper, we discuss some issues of extrinsic motivation in learning and argue that mechanism design can be applied on coursework grading to create incentives for student learning. We present an exploratory study using non-graded coursework in two fourth year university classes. Based on the study results, we discuss and suggest some important factors that influence students’ learning motives and that need to be taken into consideration for designing a proper coursework grading mechanism.

In this paper, we describe our goal of an effective course management system for assisting course managers to make informed decisions about what materials should be most appropriate to be presented to students (learners) and what learning strategies or methods should be used for the students. The system is supported by our design of a novel framework for user-driven data analytics in the cloud. Different modules of the framework will be illustrated in detail in the context of course management.

In this paper, we propose a trust opinion aggregation scheme in vehicular ad-hoc networks, to support trust models used to evaluate the quality of information shared among peers in the environment. Our scheme extends an existing identity-based aggregate signature algorithm to correctly combine signatures for multiple messages into one aggregate signature and eliminate signature redundancy. As a result, our proposed scheme is secure and archives both space efficiency and time efficiency, as confirmed by our comparative analysis.

In this paper, we propose a novel community-based approach for web service selection where super-agents with more capabilities serve as community managers. They maintain communities and build community-based reputation for a service based on the opinions from all community members that have similar interests and judgement criteria. The community-based reputation is useful for consumer agents in selecting satisfactory services when they do not have much personal experience with the services. Experimental results show that our approach results in more effective service selection. A practical reward mechanism is also introduced to create incentives for super-agents to contribute their resources and provide truthful community-based reputation information, as strong support for our approach.

A qualitative way is desirable for web service selection according to agents' preferences on non-functional Quality of Service (QoS) attributes of services. However, it is challenging when the decision has to be made for multiple agents with preferences on attributes that may be incomplete. In this paper, we first use a qualitative graphical representation tool called CP-nets to describe preference relations in a relatively compact, intuitive and structured manner. We then propose algorithms based on this representation to select web services for multiple agents despite the presence of incompleteness in their preference orderings. Our experimental results indicate that this method can always obtain some optimal outcomes which closely satisfy all agents.

The accuracy of detecting intrusions within an intrusion detection network (IDN) depends on the efficiency of collaboration between the peer intrusion detection systems (IDSes) as well as the security itself of the IDN against insider threats. In this paper, we study host-based IDNs and introduce a Dirichlet-based model to measure the level of trustworthiness among peer IDSes according to their mutual experience. The model has strong scalability properties and is robust against common insider threats, such as a compromised or malfunctioning peer. We evaluate our system based on a simulated collaborative host-based IDS network. The experimental results demonstrate the improved robustness, efficiency, and scalability of our system in detecting intrusions in comparison with existing models.

On participatory Websites, users provide opinions about products, with both overall ratings and textual reviews. In this paper, we propose an approach to accurately estimate feature ratings of the products. This approach selects user reviews that extensively discuss specific features of the products (called specialized reviews), using information distance of reviews on the features. Experiments on real data show that overall ratings of the specialized reviews can be used to represent their feature ratings. The average of these overall ratings can be used by recommender systems to provide feature specific recommendations that better help users make purchasing decisions.

The increasing number of service providers on the Web makes it challenging to select a provider for a specific service demand. Each service consumer has different expectations for a given service in different contexts, so the selection process should be consumer‐oriented and context‐dependent. Current approaches for service selection typically have consumers receive ratings of providers from other consumers, where the ratings reflect the consumers' overall subjective opinions. This may be misleading if consumers have different contexts and satisfaction criteria. In this paper, we propose that consumers objectively record their experiences, using an ontology to capture subtle details. This can then be interpreted by consumers according to their own criteria and contexts. We then integrate a method for addressing consumers who lie about their experiences, filtering them out during service selection. We demonstrate the value of our approach through experiments comparing our model with three recent rating‐based service selection models. Our experiments show that using the proposed approach, service consumers can select the service providers for their needs more accurately even if the consumers have different criteria, they change the contexts of their service demands over time, or a significant portion of them are liars.

The unfair rating problem exists when a buying agent models the trustworthiness of selling agents by also relying on ratings of the sellers from other buyers. Different probabilistic approaches have been proposed to cope with this issue. In this paper, we first summarize these approaches and provide a detailed categorization of them. This includes our own "personalized" approach for addressing this problem. Based on the implication of such analysis, we then focus on experimental comparison of our approach with two key models in a framework that simulates a dynamic electronic marketplace environment. We specifically examine different scenarios, including ones where the majority of buyers are dishonest, buyers lack personal experience with sellers, sellers may vary their behavior, and buyers may provide a large number of ratings. Our study provides the basis for deciding which approach is most appropriate to employ, in which scenario.

In the context of the Semantic Web, it may be beneficial for a user (consumer) to receive ratings from other users (advisors) regarding the reliability of an information source (provider). We offer a method for building more effective social networks of trust by critiquing the ratings provided by the advisors. Our approach models the consumer's private reputations of advisors based on ratings for providers whom the consumer has had experience with. It models public reputations of the advisors according to all ratings from these advisors for providers, including those who are unknown to the consumer. We then combine private and public reputations by assigning weights for each of them. Experimental results demonstrate that our approach is robust even when there are large numbers of advisors providing large numbers of unfair ratings. We show that we can effectively model the trustworthiness of advisors even when the population of providers grows increasingly large and discuss how our approach is beneficial in modeling providers. As such, we present a framework for sharing ratings of possibly unreliable sources, of value as users on the Semantic Web attempt to critique the trustworthiness of the information they seek.

In electronic marketplaces populated by self-interested agents, buyer agents would benefit by modeling the reputation of seller agents, in order to make effective decisions about which agents to trust. One method for representing reputation is to ask other agents in the system (called advisor agents) to provide ratings of the seller agents. The problem of unfair ratings exists in almost every reputation system, including both unfairly high and unfairly low ratings. We begin by surveying some existing approaches to this problem, characterizing their capabilities and categorizing them in terms of two main dimensions: public-private and global-local. The impact of reputation system architectures on approach selection is also discussed. Based on the study, we propose a novel personalized approach for effectively handling unfair ratings in an enhanced centralized reputation system. Experimental results demonstrate that the approach effectively adjusts the trustworthiness of advisor agents according to the percentages of unfair ratings provided by them. We then argue for the merits of our model as the basis for designing social networks to share reputation ratings of sellers in electronic marketplaces.

Familiarity between agents is often considered to be an important factor in determining the level of trust. In electronic marketplaces, trust is modeled, for instance, in order to allow buying agents to make effective selection of selling agents. In previous research, familiarity between two agents has been simply assumed to be the similarity between them, which is fixed for the two agents. We propose an improved familiarity measurement based on the exploration of factors that affect a human's feelings of familiarity and the mapping from those factors to the properties of agent societies. We examine the trust model in the context of a multiagent system within an e-commerce framework. We also carry out experiments to compare the stability of the system using the trust model with the improved familiarity measurement and that with the fixed familiarity values. Experimental results show that the stability of the system is increased by 33.47% through the improved familiarity measurement.