Large Language Models (LLMs) have been extensively applied in various recommendation scenarios, including bundle generation, thanks to their exceptional reasoning capabilities and comprehensive knowledge. However, exploiting large-scale LLMs for bundle generation introduces significant efficiency challenges—primarily high computational costs during fine-tuning and inference due to their massive parameterization. Knowledge Distillation (KD) offers a promising solution by transferring expertise from large teacher models to more compact student models. This study systematically investigates KD approaches for bundle generation with the goal of minimizing computational demands while preserving performance. Specifically, we explore three critical research questions: (1) how does the format of distilled knowledge impact bundle generation performance? (2) to what extent does the quantity of distilled knowledge influence the performance? and (3) how do different ways of utilizing the distilled knowledge affect the performance? To support this investigation, we propose a comprehensive KD framework that (i) progressively extracts knowledge from raw data in increasingly complex forms, i.e., frequent patterns \(\rightarrow\) formalized rules \(\rightarrow\) deep thoughts; (ii) captures varying quantities of distilled knowledge through different sampling strategies, multi-domain accumulation, and multi-format aggregation; and (iii) exploits complementary LLM adaptation techniques—in-context learning, supervised fine-tuning, and their combination—to leverage the distilled knowledge for domain-specific adaptation and enhanced efficiency in small student models. Through extensive experiments on multiple real-world datasets, we provide valuable insights into how knowledge format, quantity, and utilization methods collectively shape the performance of LLM-based bundle generation, which exhibits the significant potential of KD for more efficient yet effective LLM-based bundle generation.

Rumor blocking approaches in social networks aim to identify a small set of counter-rumor seed nodes and compete with rumor cascades to quickly stop the propagation of rumors. However, current rumor blocking methods assume complete knowledge of rumor node positions, which is often unattainable in real-world scenarios. In this paper, we introduce the concept of Uncertainty Rumor Blocking, where we address the uncertainty surrounding rumor node locations by considering a set of suspicious nodes, each associated with a probability indicating the likelihood of rumor propagation. As traditional node selection algorithms become inadequate under uncertain conditions, we propose a Graph Neural Network-based Inverse Reinforcement Learning (G-IRL) approach to effectively select counter-rumor seed nodes. Through comprehensive experimentation on three datasets, we demonstrate the consistent superiority of our G-IRL over state-of-the-art baseline methods for node selection in the context of uncertainty rumor containment.

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.

In cloud-edge-end (CEE) collaboration, the resource optimization based on deep reinforcement learning have achieved significant performance improvements in time-slot systems. However, some studies only focus on computing delay and energy consumption in each time slot, ignoring the impact of task backlog queues on system performance. In addition, the delay-oriented optimization tends to offload a large number of tasks to servers, failing to fully utilize the computing resource of mobile devices. To address these issues, we propose the multi-dimensional preference-driven resource optimization framework (MPROF). This study includes several key points: 1) constructing a three-layer heterogeneous architecture that applying the collaboration among edges for CEE; 2) proposing the task backlog estimation mechanism, which mitigates the impact of previous unfinished tasks on the current time slot; 3) proposing the group relative direct-preference policy optimization (GRDPO) that incorporates the preference information for efficient task offloading, and combines it with mathematical programming for the system resource optimization. The simulation experiments are conducted across multiple typical scenarios. The results show that, the proposed framework outperforms existing mainstream methods in task offloading, system delay, task backlog, and energy consumption control, demonstrating certain practical application prospects.

Infectious diseases have historically had profound effects on global health, economies, and social structures. Effective tracing of infectious diseases is essential not only for immediate public health responses but also for shaping future prevention strategies. Traditional tracing methods often emphasize homogeneous networks, overlooking the diverse transmission characteristics of heterogeneous populations. This research addresses two critical challenges: the heterogeneity of transmission across various media and modes, and the significant yet underexplored influence of community structures on epidemic spread and tracing.We propose a Heterogeneous Hypergraph Attention Network (HHAN) modelthat accounts for multiple transmission pathways and patterns within heterogeneous networks. HHAN integrates a heterogeneous graph neural network module to handle the complexity of communication among different populations, and an Agent-Based Modeling Module that combines agent-based ideas to model individual behaviors. This approach effectively captures complex interactions within community structures and addresses individual variability. Experimental results on three real-world datasets demonstrate that the HHAN model significantly outperforms other state-of-the-art methods in tackling the complex challenge of tracing infectious diseases in heterogeneous populations.

To mitigate data sparsity in Sequential Recommendation, Cross-Domain Sequential Recommendation (CDSR) exploits dynamic knowledge transfer across domains. Traditional CDSR approaches merge specific-domain sequences into mixed-domain sequences to reconnect users' dispersed interests. However, most methods rely on unidirectional transfer between mixed and specific domains on each domain task, overlooking the complex interplay between mixed-domain and domain-specific dynamics. Moreover, token-level transfer between coinciding domain sequences fails to consider inherent sequential dynamics. To address these limitations, we propose Multi-Domain Enhancement via Residual Interwoven Transfer (MERIT). Specifically, MERIT enhances domain representations along multiple domain-to-domain paths, leveraging the proposed extended cross-attention fusion compatible with partially overlapping sequences. To facilitate such transfers, MERIT further employs MoE networks in encoders to generate both intra-domain and inter-domain representations. In addition, by integrating stopped-gradient mixed-domain representations into specific-domain representations, MERIT enables the model to learn the residual signal of the mixed-domain information, better aligning with downstream specific-domain tasks. Extensive experiments on three real-world datasets demonstrate that MERIT consistently outperforms state-of-the-art CDSR counterparts with statistical significance.

Cross-Domain Sequential Recommendation (CDSR) has recently gained attention for countering data sparsity by transferring knowledge across domains.A common approach merges domain-specific sequences into cross-domain sequences, serving as bridges to connect domains.One key challenge is to correctly extract the shared knowledge among these sequences and appropriately transfer it.Most existing works directly transfer unfiltered cross-domain knowledge rather than extracting domain-invariant components and adaptively integrating them into domain-specific modelings.Another challenge lies in aligning the domain-specific and cross-domain sequences.Existing methods align these sequences based on timestamps, but this approach can cause prediction mismatches when the current tokens and their targets belong to different domains.In such cases, the domain-specific knowledge carried by the current tokens may degrade performance.To address these challenges, we propose the A-B-Cross-to-Invariant Learning Recommender (ABXI).Specifically, leveraging LoRA's effectiveness for efficient adaptation, ABXI incorporates two types of LoRAs to facilitate knowledge adaptation.First, all sequences are processed through a shared encoder that employs a domain LoRA for each sequence, thereby preserving unique domain characteristics.Next, we introduce an invariant projector that extracts domain-invariant interests from cross-domain representations, utilizing an invariant LoRA to adapt these interests into modeling each specific domain.Besides, to avoid prediction mismatches, all domain-specific sequences are aligned to match the domains of the cross-domain ground truths.

Rumor suppression is targeted at diminishing the impact of false and negative information within social networks by decreasing the prevalence of belief in such rumors among individuals, utilizing diverse strategies. Previous studies have broadly delineated rumor suppression strategies into two primary categories: targeting key nodes or edges for obstruction, and enlisting high-influence nodes to disseminate truth-related accurate information. Traditionally, employing a singular strategy involves utilizing a static algorithm throughout the rumor suppression endeavor. This method, however, encounters difficulties in adapting to fluctuating external conditions, rendering it less efficacious in the management of rumor proliferation. In response to these challenges, we introduce the concept of Adaptive Rumor Suppression (ARS), which aims to dynamically counter rumors by taking into account the nuances of propagation dynamics and the surrounding environmental context. We propose a multi-label state transition linear threshold model to more closely mirror the complex process of information diffusion across social networks. Furthermore, we advocate for a multi-round hybrid strategy that amalgamates blocking and clarification tactics to address the ARS problem within the confines of limited resource allocations. To navigate the complexities of ARS, we introduce the Hybrid Strategy of Each Round (HS-R) algorithm, which synergizes multiple strategies to effectively counter the spread of rumors. In extension, we present the Multi-Round Multi-Label (MRML) algorithm, designed to augment the efficiency of the HS-R algorithm. Experimental evaluations conducted on authentic social network datasets illustrate that our methodologies significantly outshine baseline algorithms, offering a more effective and adaptable solution to curb rumor propagation across varied environments.

With the widespread application of 5G and artificial intelligence (AI) technology, the Internet of Things (IoT) has been expanding and integrated into various aspects of our daily lives. However, this also poses challenges such as the ubiquitous demand for communication and computing resources, and data privacy issues. Considering its flexible deployment, high security, and ease of scalability, blockchain-enabled edge computing IoT network (BECIN) has become a promising solution to provide secure and fast communication and computing services. However, existing research on computation offloading in edge computing largely overlooks the stochastic arrival of computational tasks and the potential variability in the number, locations, and resource provisions of edge computing service providers. Therefore, we propose a dynamic, self-adjusting BECIN framework aimed at providing long-term stable, efficient, and secure edge computing data offloading services for ground users in a specific region. This framework supports the dynamic entry and exit of edge computing service providers. Additionally, we introduce a novel dynamic Dueling DDQN approach to update the offloading and resource management policies based on changes in resource provisioning. Experimental results demonstrate the feasibility and superior performance of our framework on system cost and system latency.

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.

Edge intelligence (EI) entails deploying artificial intelligence algorithms at the network’s edge. Utilizing edge computing infrastructure enables local data processing and decision-making, resulting in decreased latency, bandwidth consumption, and improved privacy security. With increasing the number of Internet of Things (IoT) devices and the development of 6G communication technologies, there is a growing demand for fast, efficient, and low-latency data processing, which has led to the rise of EI. This survey comprehensively reviews and analyzes the current state of research on EI from the perspective of technological development, with a particular focus on the following key aspects: (1) We review the basic concepts of EI and its distinctions from traditional cloud computing and edge computing; (2) We explore the technological framework of EI in detail, including key technologies such as edge computing and federated learning, and analyze how these technologies integrate with modern communication technologies like IoT devices and 6G networks; (3) We discuss the challenges faced when implementing EI technologies, such as data privacy and security issues, device resource constraints, and propose corresponding solutions or research directions. The survey also outlines the main research directions and technical challenges driving the future development of EI, providing valuable insights and guidance for researchers and practitioners in the field.

Most existing bundle generation approaches fall short in generating fixed-size bundles. Furthermore, they often neglect the underlying user intents reflected by the bundles in the generation process, resulting in less intelligible bundles. This paper addresses these limitations through the exploration of two interrelated tasks, i.e., personalized bundle generation and the underlying intent inference, based on different user sessions. Inspired by the reasoning capabilities of large language models (LLMs), we propose an adaptive in-context learning paradigm, which allows LLMs to draw tailored lessons from related sessions as demonstrations, enhancing the performance on target sessions. Specifically, we first employ retrieval augmented generation to identify nearest neighbor sessions, and then carefully design prompts to guide LLMs in executing both tasks on these neighbor sessions. To tackle reliability and hallucination challenges, we further introduce (1) a self-correction strategy promoting mutual improvements of the two tasks without supervision signals and (2) an auto-feedback mechanism for adaptive supervision based on the distinct mistakes made by LLMs on different neighbor sessions. Thereby, the target session can gain customized lessons for improved performance by observing the demonstrations of its neighbor sessions. Experiments on three real-world datasets demonstrate the effectiveness of our proposed method.

With increasing frequency of high-profile privacy breaches in various online platforms, users are becoming more concerned about their privacy. And recommender system is the core component of online platforms for providing personalized service, consequently, its privacy preservation has attracted great attention. As the gold standard of privacy protection, differential privacy has been widely adopted to preserve privacy in recommender systems. However, existing differentially private recommender systems only consider static and independent interactions, so they cannot apply to sequential recommendation where behaviors are dynamic and dependent. Meanwhile, little attention has been paid on the privacy risk of sensitive user features, most of them only protect user feedbacks. In this work, we propose a novel DIfferentially Private Sequential recommendation framework with a noisy Graph Neural Network approach (denoted as DIPSGNN) to address these limitations. To the best of our knowledge, we are the first to achieve differential privacy in sequential recommendation with dependent interactions. Specifically, in DIPSGNN, we first leverage piecewise mechanism to protect sensitive user features. Then, we innovatively add calibrated noise into aggregation step of graph neural network based on aggregation perturbation mechanism. And, this noisy graph neural network can protect sequentially dependent interactions and capture user preferences simultaneously. Extensive experiments demonstrate the superiority of our method over state-of-the-art differentially private recommender systems in terms of better balance between privacy and accuracy.

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 session-based recommender systems (SBRSs) mainly focus on maximizing recommendation accuracy, while few studies have been devoted to improve diversity beyond accuracy. Meanwhile, it is unclear how the accuracy-oriented SBRSs perform in terms of diversity. In addition, the asserted “tradeoff” relationship between accuracy and diversity has been increasingly questioned in the literature. Toward the aforementioned issues, we conduct a holistic study to particularly examine the recommendation performance of representative SBRSs w.r.t. both accuracy and diversity, striving for better understanding of the diversity-related issues for SBRSs and providing guidance on designing diversified SBRSs. Particularly, for a fair and thorough comparison, we deliberately select state-of-the-art non-neural, deep neural, and diversified SBRSs by covering more scenarios with appropriate experimental setups, e.g., representative datasets, evaluation metrics, and hyper-parameter optimization technique. The source code can be obtained via github.com/qyin863/Understanding-Diversity-in-SBRSs . Our empirical results unveil that (1) non-diversified methods can also obtain satisfying performance on diversity, which can even surpass diversified ones, and (2) the relationship between accuracy and diversity is quite complex. Besides the “tradeoff” relationship, they can be positively correlated with each other, that is, having a same-trend (win–win or lose–lose) relationship, which varies across different methods and datasets. Additionally, we further identify three possible influential factors on diversity in SBRSs (i.e., granularity of item categorization, session diversity of datasets, and length of recommendation lists) and offer an intuitive guideline and a potential solution regarding learned item embeddings for more effective session-based recommendation.

With the widespread popularity of the Internet of Things and various intelligent medical devices, the amount of medical data is rising sharply, and thus medical data processing has become increasingly challenging. Mobile edge computing technology allows computing power to be allocated at the edge closer to users, which enables efficient data offloading for healthcare systems. However, existing studies on medical data offloading seldom guarantee effective data privacy and security. Moreover, the research equipping data offloading architectures with Blockchain neglect the delay and energy consumption costs incurred in using Blockchain technology for medical data offloading. Therefore, in this paper, we propose a data offloading scheme for healthcare based on Blockchain technology, which achieves optimal medical resource allocation and simultaneously minimizes the cost of offloading tasks. Specifically, we design a smart contract to ensure secure data offloading. And, we formulate the cost problem as a Markov Decision Process, solved by a policy search-based deep reinforcement learning (Asynchronous Advantage Actor-Critic) scheme, where we jointly consider offloading decisions, allocation of computing resources and radio transmission bandwidth, and Blockchain data security audits. The security of our smart-contract-based mechanism is theoretically and empirically proved, while extensive experimental results also show that our solution can obtain superior performance gains with lower cost than other baselines.

Product bundling represents a prevalent marketing strategy in both offline stores and e-commerce systems. Despite its widespread use, previous studies on bundle recommendation face two significant limitations. Firstly, they rely on noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session. Secondly, they target specific tasks by holding unrealistic assumptions, e.g., the availability of bundles for recommendation directly. This paper proposes to take a step back and considers the process of bundle recommendation from a holistic user experience perspective. We first construct high-quality bundle datasets with rich metadata, particularly bundle intents, through a carefully designed crowd-sourcing task. We then define a series of tasks that together, support all key steps in a typical bundle recommendation process, from bundle detection, completion and ranking, to explanation and auto-naming, whereby 19 research questions are raised correspondingly to guide the analysis. Finally, we conduct extensive experiments and analyses with representative recommendation models and large language models (LLMs), demonstrating the challenges and opportunities, especially with the emergence of LLMs. To summarize, our study contributes by introducing novel data sources, paving the way for new research avenues, and offering insights to guide product bundling in real e-commerce platforms.

Session-based recommendation, which has witnessed a booming interest recently, focuses on predicting a user’s next interested item(s) based on an anonymous session. Most existing studies adopt complex deep learning techniques (e.g., graph neural networks) for effective session-based recommendation. However, they merely address co-occurrence between items, but fail to distinguish a causality and correlation relationship. Considering the varied interpretations and characteristics of causality and correlation relationships between items, in this study, we propose a novel method denoted as CGSR by jointly modeling causality and correlation relationships between items. In particular, we construct cause, effect, and correlation graphs from sessions by simultaneously considering the false causality problem. We further design a graph neural network–based method for session-based recommendation. To conclude, we strive to explore the relationship between items from specific “causality” (directed) and “correlation” (undirected) perspectives. Extensive experiments on three datasets show that our model outperforms other state-of-the-art methods in terms of recommendation accuracy. Moreover, we further propose an explainable framework on CGSR and demonstrate the explainability of our model via case studies on an Amazon dataset.

The motivations of users to make interactions can be divided into static preference and dynamic interest. To accurately model user representations over time, recent studies in sequential recommendation utilize information propagation and evolution to mine from batches of arriving interactions. However, they ignore the fact that people are easily influenced by the recent actions of other users in the contextual scenario, and applying evolution across all historical interactions dilutes the importance of recent ones, thus failing to model the evolution of dynamic interest accurately. To address this issue, we propose a Context-Aware Pseudo-Multi-Task Recommender System (CPMR) to model the evolution in both historical and contextual scenarios by creating three representations for each user and item under different dynamics: static embedding, historical temporal states, and contextual temporal states. To dually improve the performance of temporal states evolution and incremental recommendation, we design a Pseudo-Multi-Task Learning (PMTL) paradigm by stacking the incremental single-target recommendations into one multi-target task for joint optimization. Within the PMTL paradigm, CPMR employs a shared-bottom network to conduct the evolution of temporal states across historical and contextual scenarios, as well as the fusion of them at the user-item level. In addition, CPMR incorporates one real tower for incremental predictions, and two pseudo towers dedicated to updating the respective temporal states based on new batches of interactions. Experimental results on four benchmark recommendation datasets show that CPMR consistently outperforms state-of-the-art baselines and achieves significant gains on three of them. The source code is available at https://github.com/DiMarzioBian/CPMR.

Session-based recommendation (SR) has gained increasing attention in recent years. Quite a great amount of studies have been devoted to designing complex algorithms to improve recommendation performance, where deep learning methods account for the majority. However, most of these methods are black-box ones and ignore to provide moderate explanations to facilitate users’ understanding, which thus might lead to lowered user satisfaction and reduced system revenues. Therefore, in our study, we propose a generic Reinforced Explainable framework with Knowledge graph for Session-based recommendation (i.e., REKS), which strives to improve the existing black-box SR models (denoted as non-explainable ones) with Markov decision process. In particular, we construct a knowledge graph with session behaviors and treat SR models as part of the policy network of Markov decision process. Based on our particularly designed state vector, reward strategy, and loss function, the reinforcement learning (RL)-based framework not only achieves improved recommendation accuracy, but also provides appropriate explanations at the same time. Finally, we instantiate the REKS in five representative, state-of-the-art SR models (i.e., GRU4REC, NARM, SR-GNN, GCSAN, BERT4REC), whereby extensive experiments towards these methods on four datasets demonstrate the effectiveness of our framework on both recommendation and explanation tasks.

Information diffusion problems, such as the spread of epidemics or rumors, are widespread in society. The inverse problems of graph diffusion, which involve locating the sources and identifying the paths of diffusion based on currently observed diffusion graphs, are crucial to controlling the spread of information. The problem of localizing the source of diffusion is highly ill-posed, presenting a major obstacle in accurately assessing the uncertainty involved. Besides, while comprehending how information diffuses through a graph is crucial, there is a scarcity of research on reconstructing the paths of information propagation. To tackle these challenges, we propose a probabilistic model called DDMSL (Discrete Diffusion Model for Source Localization). Our approach is based on the natural diffusion process of information propagation over complex networks, which can be formulated using a message-passing function. First, we model the forward diffusion of information using Markov chains. Then, we design a reversible residual network to construct a denoising-diffusion model in discrete space for both source localization and reconstruction of information diffusion paths. We provide rigorous theoretical guarantees for DDMSL and demonstrate its effectiveness through extensive experiments on five real-world datasets.

Conventional matrix factorization relies on centralized collection of users’ data for recommendation, which might introduce an increased risk of privacy leakage especially when the recommender is untrusted. Existing differentially private matrix factorization methods either assume the recommender is trusted, or can only provide a uniform level of privacy protection for all ratings in untrusted recommender scenario. In this paper, we propose a novel Heterogeneous Differentially Private Matrix Factorization algorithm (HDPMF) for untrusted recommender. To the best of our knowledge, we are the first to achieve heterogeneous differential privacy for decentralized matrix factorization in untrusted recommender scenario. Specifically, our framework uses modified stretching mechanism with an innovative rescaling scheme to achieve better trade-off between privacy and accuracy. Meanwhile, by allocating privacy budget properly, we can capture homogeneous privacy preference within a user/item but heterogeneous privacy preference across different users/items. Theoretical analysis confirms that HDPMF renders rigorous privacy guarantee, and exhaustive experiments demonstrate its superiority especially in strong privacy guarantee, high dimension model and sparse dataset scenario.

MicroRNAs (miRNAs) are a family of non-coding RNA molecules with vital roles in regulating gene expression. Although researchers have recognized the importance of miRNAs in the development of human diseases, it is very resource-consuming to use experimental methods for identifying which dysregulated miRNA is associated with a specific disease. To reduce the cost of human effort, a growing body of studies has leveraged computational methods for predicting the potential miRNA-disease associations. However, the extant computational methods usually ignore the crucial mediating role of genes and suffer from the data sparsity problem. To address this limitation, we introduce the multi-task learning technique and develop a new model called MTLMDA (Multi-Task Learning model for predicting potential MicroRNA-Disease Associations). Different from existing models that only learn from the miRNA-disease network, our MTLMDA model exploits both miRNA-disease and gene-disease networks for improving the identification of miRNA-disease associations. To evaluate model performance, we compare our model with competitive baselines on a real-world dataset of experimentally supported miRNA-disease associations. Empirical results show that our model performs best using various performance metrics. We also examine the effectiveness of model components via ablation study and further showcase the predictive power of our model for six types of common cancers. The data and source code are available from https://github.com/qwslle/MTLMDA.

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.

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.

Product bundling is a commonly-used marketing strategy in both offline retailers and online e-commerce systems. Current research on bundle recommendation is limited by: (1) noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session; and (2) specific tasks, holding unrealistic assumptions, e.g., the availability of bundles for recommendation directly. In this paper, we propose to take a step back and consider the process of bundle recommendation from a holistic user experience perspective. We first construct high-quality bundle datasets with rich meta information, particularly bundle intents, through a carefully designed crowd-sourcing task. We then define a series of tasks that together, support all key steps in a typical bundle recommendation process, from bundle detection, completion, ranking, to explanation and auto-naming. Finally, we conduct extensive experiments and in-depth analysis that demonstrate the challenges of bundle recommendation, arising from the need for capturing complex relations among users, products, and bundles, as well as the research opportunities, especially in graph-based neural methods. To sum up, our study delivers new data sources, opens up new research directions, and provides useful guidance for product bundling in real e-commerce platforms. Our datasets are available at GitHub (\urlhttps://github.com/BundleRec/bundle_recommendation ).

With the rapid information explosion on online social network sites (SNSs), it becomes difficult for users to seek new friends or broaden their social networks in an efficient way. Link prediction, which can effectively conquer this problem, has thus attracted wide attention. Previous methods on link prediction fail to comprehensively capture the factors leading to new link formation: 1) few models have considered the varied impacts of users' short-term and long-term interests on link prediction. Besides, they fail to jointly model the influence from social influence and “weak links”; 2) considering that different factors should be derived from information sources of different modalities, there is a lack of effective multi-modal framework for link prediction. In this view, we propose a novel multi-modal framework for link prediction (referred as LP-UIT) which fuses a comprehensive set of features (i.e., user information and topological features) extracted from multi-modal information (i.e., textual information, graph information, and numerical information). Specifically, we adopt graph convolutional network to process the network information to capture topological features, employ natural language processing techniques (i.e., TF-IDF and word2Vec) to model users' short-term and long-term interests, and identify social influence and “weak links” from numerical features. We further use an attention mechanism to model the relationship between textual and topological features. Finally, a multi-layer perceptron (MLP) is designed to combine the representations from three modalities for link prediction. Extensive experiments on two real-world datasets demonstrate the superiority of LP-UIT over the state-of-the-art methods.

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).

In the field of sequential recommendation, deep learning--(DL) based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ones. However, there is little systematic study on DL-based methods, especially regarding how to design an effective DL model for sequential recommendation. In this view, this survey focuses on DL-based sequential recommender systems by taking the aforementioned issues into consideration. Specifically, we illustrate the concept of sequential recommendation, propose a categorization of existing algorithms in terms of three types of behavioral sequences, summarize the key factors affecting the performance of DL-based models, and conduct corresponding evaluations to showcase and demonstrate the effects of these factors. We conclude this survey by systematically outlining future directions and challenges in this field.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.