Digital Twins (DTs) offer powerful tools for managing complex infrastructure systems, but their effectiveness is often limited by challenges in integrating unstructured knowledge. Recent advances in Large Language Models (LLMs) bring new potential to address this gap, with strong abilities in extracting and organizing diverse textual information. We therefore propose LSDTs (LLM-Augmented Semantic Digital Twins), a framework that helps LLMs extract planning knowledge from unstructured documents like environmental regulations and technical guidelines, and organize it into a formal ontology. This ontology forms a semantic layer that powers a digital twin—a virtual model of the physical system—allowing it to simulate realistic, regulation-aware planning scenarios. We evaluate LSDTs through a case study of offshore wind farm planning in Maryland, including its application during Hurricane Sandy. Results demonstrate that LSDTs support interpretable, regulation-aware layout optimization, enable high-fidelity simulation, and enhance adaptability in infrastructure planning. This work shows the potential of combining generative AI with digital twins to support complex, knowledge-driven planning tasks.

Environmental modeling faces critical challenges in predicting ecosystem dynamics across unmonitored regions due to limited and geographically imbalanced observation data. This challenge is compounded by spatial heterogeneity, causing models to learn spurious patterns that fit only local data. Unlike conventional domain generalization, environmental modeling must preserve invariant physical relationships and temporal coherence during augmentation. In this paper, we introduce Generalizable Representation Enhancement via Auxiliary Transformations (GREAT), a framework that effectively augments available datasets to improve predictions in completely unseen regions. GREAT guides the augmentation process to ensure that the original governing processes can be recovered from the augmented data, and the inclusion of the augmented data leads to improved model generalization. Specifically, GREAT learns transformation functions at multiple layers of neural networks to augment both raw environmental features and temporal influence. They are refined through a novel bi-level training process that constrains augmented data to preserve key patterns of the original source data. We demonstrate GREAT's effectiveness on stream temperature prediction across six ecologically diverse watersheds in the eastern U.S., each containing multiple stream segments. Experimental results show that GREAT significantly outperforms existing methods in zero-shot scenarios. This work provides a practical solution for environmental applications where comprehensive monitoring is infeasible.

The recent advancements in Deep Reinforcement Learning (DRL) have significantly improved the performance of adaptive Traffic Signal Control (TSC). However, DRL policies are typically represented by over-parameterized neural networks, which function as black-box models. Consequently, the learned policies often lack interpretability and are challenging to deploy on resource-constrained edge hardware. Moreover, the DRL methods frequently exhibit poor generalization, struggling to transfer the learned policies across different geographical regions. These limitations hinder the real-world applicability of learning-based approaches. To address these issues, we suggest the use of an inherently interpretable program for representing the control policy. We present Programmatic Interpretable reinforcement learning for effective traffic signal control ($\pi$-eLight), a new approach designed to autonomously discover non-differentiable programs. Specifically, we first define an effective program framework as the control policy, where certain components remain learnable. Next, we introduce a Domain Specific Language (DSL) for constructing interpretable programs and transformation rules for generating programs with hierarchical structures. Last, we utilize Monte Carlo Tree Search (MCTS) to find the optimal program in a discrete space. Extensive experiments demonstrate that$\pi$-eLight consistently outperforms DRL-based baselines while exhibiting superior generalization across intersections in different cities. Moreover, the learned programmatic policies can be directly deployed on edge devices with minimal computational resources, further enhancing real-world applicability.

Recent advances in Large Language Models (LLMs) have demonstrated significant potential in the field of Recommendation Systems (RSs). Most existing studies have focused on converting user behavior logs into textual prompts and leveraging techniques such as prompt tuning to enable LLMs for recommendation tasks. Meanwhile, research interest has recently grown in multimodal recommendation systems that integrate data from images, text, and other sources using modality fusion techniques. This introduces new challenges to the existing LLM-based recommendation paradigm which relies solely on text modality information. Moreover, although Multimodal Large Language Models (MLLMs) capable of processing multi-modal inputs have emerged, how to equip MLLMs with multi-modal recommendation capabilities remains largely unexplored. To this end, in this paper, we propose the Multimodal Large Language Model-enhanced Sequential Multimodal Recommendation (MLLM-MSR) model. To capture the dynamic user preference, we design a two-stage user preference summarization method. Specifically, we first utilize an MLLM-based item-summarizer to extract image feature given an item and convert the image into text. Then, we employ a recurrent user preference summarization generation paradigm to capture the dynamic changes in user preferences based on an LLM-based user-summarizer. Finally, to enable the MLLM for multi-modal recommendation task, we propose to fine-tune a MLLM-based recommender using Supervised Fine-Tuning (SFT) techniques. Extensive evaluations across various datasets validate the effectiveness of MLLM-MSR, showcasing its superior ability to capture and adapt to the evolving dynamics of user preferences.

Water temperature can vary substantially even across short distances within the same sub-watershed. Accurate prediction of stream water temperature at fine spatial resolutions (i.e., fine scales, ≤ 1 km) enables precise interventions to maintain water quality and protect aquatic habitats. Although spatiotemporal models have made substantial progress in spatially coarse time series modeling, challenges persist in predicting at fine spatial scales due to the lack of data at that scale. To address the problem of insufficient fine-scale data, we propose a Multi-Scale Graph Learning (MSGL) method. This method employs a multi-task learning framework where coarse-scale graph learning, bolstered by larger datasets, simultaneously enhances fine-scale graph learning. Although existing multi-scale or multi-resolution methods integrate data from different spatial scales, they often overlook the spatial correspondences across graph structures at various scales. To address this, our MSGL introduces an additional learning task, cross-scale interpolation learning, which leverages the hydrological connectedness of stream locations across coarse- and fine-scale graphs to establish cross-scale connections, thereby enhancing overall model performance. Furthermore, we have broken free from the mindset that multi-scale learning is limited to synchronous training by proposing an Asynchronous Multi-Scale Graph Learning method (ASYNC-MSGL). Extensive experiments demonstrate the state-of-the-art performance of our method for anti-sparse downscaling of daily stream temperatures in the Delaware River Basin, USA, highlighting its potential utility for water resources monitoring and management.

Physics-guided machine learning (PGML) has become a prevalent approach in studying scientific systems due to its ability to integrate scientific theories for enhancing machine learning (ML) models. However, most PGML approaches are tailored to isolated and relatively simple tasks, which limits their applicability to complex systems involving multiple interacting processes and numerous influencing features. In this paper, we propose a Physics-Guided Foundation Model (PGFM) that combines pre-trained ML models and physics-based models and leverages their complementary strengths to improve the modeling of multiple coupled processes. To effectively conduct pre-training, we construct a simulated environmental system that encompasses a wide range of influencing features and various simulated variables generated by physics-based models. The model is pre-trained in this system to adaptively select important feature interactions guided by multi-task objectives. We then fine-tune the model for each specific task using true observations, while maintaining consistency with established physical theories, such as the principles of mass and energy conservation. We demonstrate the effectiveness of this methodology in modeling water temperature and dissolved oxygen dynamics in real-world lakes. The proposed PGFM is also broadly applicable to a range of scientific fields where physics-based models are being used.

Efficient simulation is essential for enhancing proactive preparedness for sudden-onset disasters such as earthquakes.Recent advancements in large language models (LLMs) as world models show promise in simulating complex scenarios.This study examines multiple LLMs to proactively estimate perceived earthquake impacts.Leveraging multimodal datasets including geospatial, socioeconomic, building, and street-level imagery data, our framework generates Modified Mercalli Intensity (MMI) predictions at zip code and county scales.Evaluations on the 2014 Napa and 2019 Ridgecrest earthquakes using USGS "Did You Feel It? (DYFI)" reports demonstrate significant alignment, as evidenced by a high correlation of 0.88 and a low RMSE of 0.77 as compared to real reports at the zip code level.Techniques such as retrieval-augmented generation (RAG) and in-context learning (ICL) can improve simulation performance, while visual inputs notably enhance accuracy compared to structured numerical data alone.These findings show the promise of LLMs in simulating disaster impacts that can help strengthen preevent planning.

Understanding environmental ecosystems is vital for the sustainable management of our planet. However, existing physics-based and data-driven models often fail to generalize to varying spatial regions and scales due to the inherent data heterogeneity presented in real environmental ecosystems. This generalization issue is further exacerbated by the limited observation samples available for model training. To address these issues, we propose Geo-STARS, a geo-aware spatio-temporal modeling framework for predicting stream water temperature across different watersheds and spatial scales. The major innovation of Geo-STARS is the introduction of geo-aware embedding, which leverages geographic information to explicitly capture shared principles and patterns across spatial regions and scales. We further integrate the geo-aware embedding into a gated spatio-temporal graph neural network. This design enables the model to learn complex spatial and temporal patterns guided by geographic and hydrological context, even with sparse or no observational data. We evaluate Geo-STARS's efficacy in predicting stream water temperature, which is a master factor for water quality. Using real-world datasets spanning 37 years across multiple watersheds along the eastern coast of the United States, Geo-STARS demonstrates its superior generalization performance across both regions and scales, outperforming state-of-the-art baselines. These results highlight the promise of Geo-STARS for scalable, data-efficient environmental monitoring and decision-making.

The discovery of environmental knowledge depends on labeled task-specific data, but is often constrained by the high cost of data collection. Existing machine learning approaches usually struggle to generalize in data-sparse or atypical conditions. To this end, we propose an Augmentation-Adaptive Self-Supervised Learning (A2SL) framework, which retrieves relevant observational samples to enhance modeling of the target ecosys-tem. Specifically, we introduce a multi-level pairwise learning loss to train a scenario encoder that captures varying degrees of similarity among scenarios. These learned similarities drive a retrieval mechanism that supplements a target scenario with relevant data from different locations or time periods. Further-more, to better handle variable scenarios, particularly under atypical or extreme conditions where traditional models struggle, we design an augmentation-adaptive mechanism that selectively enhances these scenarios through targeted data augmentation. Using freshwater ecosystems as a case study, we evaluate A2SL in modeling water temperature and dissolved oxygen dynamics in real-world lakes. Experimental results show that A2SL signif-icantly improves predictive accuracy and enhances robustness in data-scarce and atypical scenarios. Although this study focuses on freshwater ecosystems, the A2SL framework offers a broadly applicable solution in various scientific domains. Code-https://github.com/shiyuanlsy/A2sl

Multimodal sentiment analysis (MSA) has shown promising results but often poses significant challenges in real-world applications due to its dependence on the complete and aligned multimodal sequences. While existing approaches attempt to address missing modalities through feature reconstruction, they often neglect the complex interplay between homogeneous and heterogeneous relationships in multimodal features. To address this problem, we propose Decoupled-Adaptive Reconstruction (DAR), a novel framework that explicitly addresses these limitations through two key components: (1) a mutual information-based decoupling module that decomposes features into common and independent representations, and (2) a reconstruction module that independently processes these decoupled features before fusion for downstream tasks. Extensive experiments on two benchmark datasets demonstrate that DAR significantly outperforms existing methods in both modality reconstruction and sentiment analysis tasks, particularly in scenarios with missing or unaligned modalities. Our results show improvements of 2.21% in bi-classification accuracy and 3.9% in regression error compared to state-of-the-art baselines on the MOSEI dataset.

Click-through Rate (CTR) prediction is crucial for online personalization platforms. Recent advancements have shown that modeling rich user behaviors can significantly improve the performance of CTR prediction. Current long-term user behavior modeling algorithms predominantly follow two cascading stages. The first stage retrieves subsequence related to the target item from the long-term behavior sequence, while the second stage models the relationship between the subsequence and the target item. Despite significant progress, these methods have two critical flaws. First, the retrieval query typically includes only target item information, limiting the ability to capture the user's diverse interests. Second, relational information, such as sequential and interactive information within the subsequence, is frequently overlooked. Therefore, it requires to be further mined to more accurately model user interests.

Environmental ecosystems exhibit complex and evolving dynamics over time, making the modeling of non-stationary processes critically important. However, traditional methods often rely on static models trained on entire datasets, failing to capture the non-stationary and drastically fluctuating characteristics. Dynamically adjusting models to evolving data is challenging, as they can easily either lag behind new trends or overfit newly received data. To address these challenges, we propose Domain-Adaptive Continual Meta-Learning (DACM) method, aiming to automatically detect distribution shifts and adapt to newly emergent domains. In particular, while DACM continuously explores the sequential temporal data, it also exploits historical data that are similar in distribution to the current observations. By striking a balance between temporal exploration and distributional exploitation, DACM quickly adjusts the model to stay up-to-date with new trends while maintaining generalization ability to data with similar distributions. We demonstrate the effectiveness of DACM on a real-world water temperature prediction dataset, where it outperforms diverse baseline models and shows strong adaptability and predictive performance in non-stationary environments.

“AI for Science” has become a prominent yet controversial research frontier, eliciting both acclaim and criticism. While celebrated for its potential to revolutionize scientific discovery, concerns persist regarding the reliability, interpretability, and validation of AI-generated knowledge. This paper explores core challenges, including the opacity of AI insights, the difficulty of evaluating unverifiable outputs, and the in-adequacy of traditional frameworks like the Turing Test. Although addressing these issues may seem like a distant goal, this paper proposes a Blue Sky Idea aimed at redefining AI’s role in scientific exploration and paving the way for transformative progress.

Transmission Control Protocol (TCP) congestion control is a fundamental mechanism in the Internet that maintains network stability and performance by adjusting the sending rate of connections. Recently, Deep Reinforcement Learning (DRL) methods have shown superior performance over traditional expert-designed solutions. However, the DRL policies are often represented by black-box neural networks, they lack interpretability, making verification challenging and requiring excessive floating-point computation. This work introduces a novel approach, Programmatic reinforcement learning for Congestion Control (ProCC), designed to autonomously discover a program as a control policy from scratch. Programs in ProCC include branching structures (e.g., if blocks and if-else blocks), conditions and actions. However, directly optimizing such program structures is challenging due to their discrete non-differentiable nature, and the program space grows exponentially as the depth increases. To address this issue, ProCC defines a Domain-Specific Language (DSL) and program transformation rules, enabling the construction of a program search graph where similar programs are closer in proximity. Subsequently, ProCC employs Monte Carlo Tree Search (MCTS) to efficiently explore the discrete space and obtain promising programs. Extensive experiments conducted in multiple simulated environments demonstrate that ProCC is adaptive and consistently performs well under varying network conditions. The learned program's performance surpasses that of state-of-the-art DRL agents, and more importantly, the generated policies are concise, transparent, and computationally efficient.

Modeling and process optimization of Multi-stage Continuous Production System (MCPS) is an important research topic in the field of intelligent manufacturing today. However, due to the inherent properties of MCPS, existing researches face difficulties in (1) coupling optimization across multiple stages under diverse constraints and objectives, and (2) accurately mapping controllable process variables to critical production outputs. In this work, we propose a novel multi-objective optimization method for MCPS based on feature fusion modeling, with main innovations including: 1) For MCPS modeling, we propose a Transformer network structure with a parallel dual-branch attention mechanism for linking process variables to production outputs. A multi-stage feature fusion prediction model based on DenseNet is developed, which not only improves prediction accuracy but also provides intermediate stage prediction results. 2) For process optimization of MCPS, we design a multi-constraint and multi-objective optimization model. Subsequently, we propose a dynamic multi-objective optimization algorithm framework to enhance the performance of the algorithm and improve the quality of solutions. Furthermore, we conducted experiments with a real Coke-Chem integrated production dataset, and the results show that our predictive model achieves an average MAE of 0.11, MSE of 0.04, and RMSE of 0.20, outperforming state-of-the-art methods and boosts solution coverage (up to 80%) and hypervolume (up to 2.5×) when integrated with classical algorithms like NSGA-II and GDE3.

This paper introduces a Process-Guided Learning (Pril) framework that integrates physical models with recurrent neural networks (RNNs) to enhance the prediction of dissolved oxygen (DO) concentrations in lakes, which is crucial for sus-taining water quality and ecosystem health. Unlike traditional RNNs, which may deliver high accuracy but often lack physical consistency and broad applicability, the Pril method incorporates differential DO equations for each lake layer, modeling it as a first-order linear solution using a forward Euler scheme with a daily timestep. However, this method is sensitive to numerical instabilities. When drastic fluctuations occur, the numerical integration is neither mass-conservative nor stable. Especially during stratified conditions, exogenous fluxes into each layer cause significant within-day changes in DO concentrations. To address this challenge, we further propose an Adaptive Process-Guided Learning (April) model, which dynamically adjusts timesteps from daily to sub-daily intervals with the aim of mitigating the discrepancies caused by variations in entrainment fluxes. April uses a generator-discriminator architecture to identify days with significant DO fluctuations and employs a multi-step Euler scheme with sub-daily timesteps to effectively manage these variations. We have tested our methods on a wide range of lakes in the Midwestern USA, and demonstrated robust capability in predicting DO concentrations even with limited training data. While primarily focused on aquatic ecosystems, this approach is broadly applicable to diverse scientific and engineering disciplines that utilize process-based models, such as power engineering, climate science, and biomedicine.

Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity for operational optimization in industry 4.0. In recent years, Deep Reinforcement Learning (DRL) based methods offer promising enhancements for sequential optimization processes and can be used for reducing carbon emissions. However, existing DRL methods need a pre-defined reward function to assess the impact of each action on the final sustainable development goals (SDG). In many real applications, such a reward function cannot be given in advance. To address the problem, this study proposes a Performance based Adversarial Imitation Learning (PAIL) engine. It is a novel method to acquire optimal operational policies for carbon neutrality without any pre-defined action rewards. Specifically, PAIL employs a Transformer-based policy generator to encode historical information and predict following actions within a multi-dimensional space. The entire action sequence will be iteratively updated by an environmental simulator. Then PAIL uses a discriminator to minimize the discrepancy between generated sequences and real-world samples of high SDG. In parallel, a Q-learning framework based performance estimator is designed to estimate the impact of each action on SDG. Based on these estimations, PAIL refines generated policies with the rewards from both discriminator and performance estimator. PAIL is evaluated on multiple real-world application cases and datasets. The experiment results demonstrate the effectiveness of PAIL comparing to other state-of-the-art baselines. In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality.

It is always challenging task for students to select right universities. For students, graduate job placement is the most important component of university quality. However, existing university evaluation methods predominantly depend on either subjective criteria, such as the perceived quality of the learning environment and academic prestige, or on factors like faculty excellence, which may not provide a precise indication of graduate job placement. Indeed, there is still a lack of a data-driven approach to accurately measure university quality based on the employment situation of graduates. Moreover, the inherently unsupervised nature of university evaluation, compounded by the absence of a reasonable ground truth, necessitates the development of a reliable supervised methodology to precisely quantify university quality. Our basic assumption is that highly influential companies would attract graduates from high-ranking universities. To this end, in this paper, we formulate university evaluation problem into the graduate flow prediction problem, and propose an Influence based Graph Autoencoder (IGAE) method to learn the representation of universities based on the employment of their graduates. Specifically, we first build a talent transition graph based on the massive resume information. This graph reveals the flow of talent between institutions. Then, considering the asymmetric and heterogeneous properties of talent flow, an unidirectional aggregation process with a heterogeneous attention mechanism is designed to encode the nodes in the directed graph and preserve the influence terms at the same time. Afterwards, a novel dual self-attention module is exploited to capture the dynamic pattern of institutions to forecast future employment. Furthermore, we design an influence based decoder to predict the existence of talent flows and estimate the frequency of employment, which can be learnt in a joint learning framework. Finally, we conduct extensive experiments on a real-world dataset for performance evaluation. The experimental results clearly validate the effectiveness of our approach compared to the state-of-the-art baselines, and we provide a case study on university influence analysis.

Federated recommendation (FedRec) can train personalized recommenders without collecting user data, but the decentralized nature makes it susceptible to poisoning attacks. Most previous studies focus on the targeted attack to promote certain items, while the untargeted attack that aims to degrade the overall performance of the FedRec system remains less explored. In fact, untargeted attacks can disrupt the user experience and bring severe financial loss to the service provider. However, existing untargeted attack methods are either inapplicable or ineffective against FedRec systems. In this paper, we delve into the untargeted attack and its defense for FedRec systems. (i) We propose ClusterAttack, a novel untargeted attack method. It uploads poisonous gradients that converge the item embeddings into several dense clusters, which make the recommender generate similar scores for these items in the same cluster and perturb the ranking order. (ii) We propose a uniformity-based defense mechanism (UNION) to protect FedRec systems from such attacks. We design a contrastive learning task that regularizes the item embeddings toward a uniform distribution. Then the server filters out these malicious gradients by estimating the uniformity of updated item embeddings. Experiments on two public datasets show that ClusterAttack can effectively degrade the performance of FedRec systems while circumventing many defense methods, and UNION can improve the resistance of the system against various untargeted attacks, including our ClusterAttack.

Collaborative filtering (CF) from implicit datasets has attracted much attention in recent years. The current mainstream pairwise methods optimize the Area Under the Curve (AUC) and are empirically proven to be helpful to exploit implicit feedback, but lead to not addressing the rank-biased scenarios where positive items are supposed to be placed on the top-k positions. Although there exist listwise methods, they have low efficiency and are not particularly adequate for general implicit feedback situations. To that end, in this paper, we propose a new framework, namely Collaborative List-and-Pairwise Filtering (CLAPF), which aims to introduce pairwise thinking into listwise methods. Specifically, we first smooth a well-known rank-biased measure called Mean Average Precision (MAP) as a low-bound version to make it can be optimized. After that, we combined the objective functions of optimizing the MAP with pairwise comparisons. The CLAPF framework is a new hybrid model that provides the idea of utilizing a listwise measure in a pairwise way on implicit feedback.

Click-Through Rate (CTR) prediction of intelligent marketing systems is of great importance, in which feature interaction selection plays a key role. Most approaches model interactions of features by the same pre-defined operation under expert guidance, among which improper interactions may bring unnecessary noise and complicate the training process. To that end, in this paper, we aim to adaptively evolve the model to select proper operations to interact on feature pairs under task guidance. Inspired by natural evolution, we propose a general Cognitive EvoLutionary Search (CELS) framework, where cognitive ability refers to the malleability of organisms to orientate to the environment. Specifically, we conceptualize interactions as genomes, models as organisms, and tasks as natural environments. Mirroring how genetic malleability develops environmental adaptability, we thus diagnose the fitness of models to simulate the survival rates of organisms for natural selection, thereby an evolution path can be planned and visualized, offering an intuitive interpretation of the mechanisms underlying interaction modeling and selection. Based on the CELS framework, we develop four instantiations including individual-based search and population-based search. We demonstrate how individual mutation and population crossover enable CELS to evolve into diverse models suitable for various tasks and data, providing ready-to-use models. Extensive experiments on real-world datasets demonstrate that CELS significantly outperforms state-of-the-art approaches.

In recent years, pairwise methods, such as Bayesian Personalized Ranking (BPR), have gained significant attention in the field of collaborative filtering for recommendation systems. Group BPR is an extension of BPR that incorporates user groups to relax the strict assumption of independence between two users. However, the reliability of its user groups may be compromised as they only focus on a few behavioral similarities. To address this problem, this paper proposes a new entropy-weighted similarity measure for implicit feedback to quantify the relation between two users and sample like-minded user groups. We first introduce the group preference into several pairwise ranking algorithms and then utilize the entropy-weighted similarity to sample groups to further improve these algorithms. Unlike other approaches that rely solely on common item ratings, our method incorporates global information into the similarity measure, resulting in a more reliable approach to group sampling. We conducted experiments on two real-world datasets and evaluated our method using different metrics. The results show that our method can construct better user groups from sparse data and produce more accurate recommendations. Our approach can be applied to a wide range of recommendation systems, and this can significantly improve the performance of pairwise ranking algorithms, making it an effective tool for pairwise ranking.

Every organization has organizational networks for exchange of ideas and information. It is believed that organizational network analysis (ONA) can help the business be more effective. While considerable research efforts have been made for visualizing and analyzing relationships in organizational networks, it lacks a holistic way to model the complex social structures and rich semantic information of these networks. Indeed, employee behaviors can occur across different communication platforms, such as email and instant messaging systems, which naturally lead to the multiplex structure of organizational social networks. Meanwhile, it is also a challenge to model the impact of semantic information, such as employee attributes and organization charts, and the collaboration relationships of employees. To this end, in this paper, we propose a Multiplex Attentive Network Embedding (MANE) approach for modeling organizational social networks in a holistic way. Specifically, we first develop a multiple attributed random walk approach to jointly model multiple networks, with the integration of external work information. Then, we preserve the network structure by maximizing the probability of predicting the central node based on the surrounding context nodes. In particular, we introduce an attention mechanism to assign a weight to each context node in the training process, according to its attributed relation and structural relation with the central node by utilizing the k-core algorithm and the shortest path algorithm. In this way, the embedding results can be kept consistent with their structural relationships. Furthermore, to solve some department-level tasks, we introduce an attentive relational transition method to learn the representation of departments in the organizational networks. Finally, we evaluate the performance of MANE with extensive experiments on real-world data for three important talent management tasks, namely employee performance prediction, employee turnover prediction and department performance prediction. We also conduct a link prediction task to validate the effectiveness of employee embedding. Experimental results clearly show the effectiveness and interpretability of MANE for organizational network analysis.

Click-through rate (CTR) prediction is fundamental in many industrial applications, such as online advertising and recommender systems. With the development of the online platforms, the sequential user behaviors grow rapidly, bringing us great opportunity to better understand user preferences.However, it is extremely challenging for existing sequential models to effectively utilize the entire behavior history of each user. First, there is a lot of noise in such long histories, which can seriously hurt the prediction performance. Second, feeding the long behavior sequence directly results in infeasible inference time and storage cost. In order to tackle these challenges, in this paper we propose a novel framework, which we name as User Behavior Clustering Sampling (UBCS). In UBCS, short sub-sequences will be obtained from the whole user history sequence with two cascaded modules: (i) Behavior Sampling module samples short sequences related to candidate items using a novel sampling method which takes relevance and temporal information into consideration; (ii) Item Clustering module clusters items into a small number of cluster centroids, mitigating the impact of noise and improving efficiency. Then, the sampled short sub-sequences will be fed into the CTR prediction module for efficient prediction. Moreover, we conduct a self-supervised consistency pre-training task to extract user persona preference and optimize the sampling module effectively. Experiments on real-world datasets demonstrate the superiority and efficiency of our proposed framework.

The implicit feedback based collaborative filtering (CF) has attracted much attention in recent years, mainly because users implicitly express their preferences in many real-world scenarios. The current mainstream pairwise methods optimize the Area Under the Curve (AUC) and are empirically proved to be helpful to exploit binary relevance data, but lead to either not address the ranking problem, or not specifically focus on top-$k$recommendation. Although there exists the listwise method maximizes the Mean Reciprocal Rank (MRR), it has low efficiency and is not particularly adequate for general implicit feedback situations. To that end, in this paper, we propose a new framework, namelyCollaborative List-and-Pairwise Filtering (CLAPF), which aims to introduce pairwise thinking into listwise methods. Specifically, we smooth another well-known rank-biased measure called Mean Average Precision (MAP), and respectively combine two rank-biased metrics (MAP, MRR) with the pairwise objective function to capture the performance of top-$k$recommendation. Furthermore, the sampling scheme for CLAPF is discussed to accelerate the convergence speed. Our CLAPF framework is a new hybrid model that provides an idea of utilizing rank-biased measures in a pairwise way on implicit feedback. Empirical studies demonstrated CLAPF outperforms state-of-the-art approaches on real-world datasets.

With the accelerated technology development, technological trend forecasting through patent mining has become a hot issue for high-tech companies. In this term, extensive attention has been attracted to forecasting technological knowledge flows (TKF), i.e., predicting the directional flows of knowledge from one technological field to another. However, existing studies either rely on labor intensive empirical analysis or do not consider the intrinsic characteristics inherent in TKF, including the double-faced aspects (i.e., act as both the source and target) of technology nodes, multiple complex relationships among different technologies, and dynamics of the TKF process. To this end, in this paper, we make a further study and propose a data-driven solution, i.e., a Hierarchical Interactive Graph Neural Network (HighTKF), to automatically find the potential flow trends of technologies. Specifically, HighTKF makes final predictions through two kinds of representations of each technology node (a diffusion vector and an absorption vector), which is realized by three components: High-Order Interaction Module (HOI), Hierarchical Delivery Module (HD) and Technology Flow Tracing Module (TFT). For one thing, HOI and HD aim to model high-order network relationships and hierarchical relationships among technologies. For another, TFT is designed for capturing the dynamic feature evolution of technologies with the above relations involved. Also, we design a hybrid loss function and propose a new evaluation metric for better predicting the unprecedented flows between technologies. Finally, we conduct extensive experiments on a real-world patent dataset, the results verify the effectiveness of our approach and reveal some interesting phenomenons on technological knowledge flow trends.

Cognitive diagnosis, a fundamental task in education area, aims at providing an approach to reveal the proficiency level of students on knowledge concepts. Actually, monotonicity is one of the basic conditions in cognitive diagnosis theory, which assumes that student's proficiency is monotonic with the probability of giving the right response to a test item. However, few of previous methods consider the monotonicity during optimization. To this end, we propose Item Response Ranking framework (IRR), aiming at introducing pairwise learning into cognitive diagnosis to well model the monotonicity between item responses. Specifically, we first use an item specific sampling method to sample item responses and construct response pairs based on their partial order, where we propose the two-branch sampling methods to handle the unobserved responses. After that, we use a pairwise objective function to exploit the monotonicity in the pair formulation. In fact, IRR is a general framework which can be applied to most of contemporary cognitive diagnosis models. Extensive experiments demonstrate the effectiveness and interpretability of our method.

One-hot encoder accompanied by a softmax loss has become the default configuration to deal with the multiclass problem, and is also prevalent in deep learning (DL) based recommender systems (RS). The standard learning process of such methods is to fit the model outputs to a one-hot encoding of the ground truth, referred to as the hard target. However, it is known that these hard targets largely ignore the ambiguity of unobserved feedback in RS, and thus may lead to sub-optimal generalization performance. In this work, we propose SoftRec, a new RS optimization framework to enhance item recommendation. The core idea is that we add additional supervisory signals - well-designed soft targets - for each instance so as to better guide the recommender learning. Meanwhile, we carefully investigate the impacts of specific soft target distributions by instantiating the SoftRec with a series of strategies, including item-based, user-based, and model-based. To verify the effectiveness of SoftRec, we conduct extensive experiments on two public recommendation datasets by using various deep recommendation architectures. The experimental results show that our methods achieve superior performance compared with the standard optimization approaches. Moreover, SoftRec could also exhibit strong performance in cold-start scenarios where user-item interaction has higher sparsity.

This paper introduces Alpha-Beta Sampling (ABS) strategy, which is particularly intended for the sampling problem of pairwise ranking in one-class collaborative filtering (PROCCF). Specifically, ABS strategy places more emphasis on such training examples, including positive item with a lower preference score and negative items with a higher preference score for each gradient step. Then, we provide the corresponding proofs for the ABS strategy from both gradient and ranking perspectives. First, we prove that sampled training examples by ABS strategy can update the model parameters with a large magnitude and analyze two instantiations by combining two specific pairwise algorithms. Second, it can be proved that ABS strategy is equivalent to optimizing for ranking-aware evaluation metrics like Normalized Discounted Cumulative Gain (NDCG). Furthermore, ABS strategy can be very general and applicable in a lot of pairwise structures of pairwise algorithms. Based on ABS strategy, we provide an effective sampling algorithm to dynamically draw items for each SGD update. Finally, we evaluate the ABS strategy by conducting sampling tasks in two representative pairwise algorithms. The experiment results show that the ABS strategy performs significantly better than the baseline strategies.

Technological change and innovation are vitally important, especially for high-tech companies. However, factors influencing their future research and development (R&D) trends are both complicated and various, leading it a quite difficult task to make technology tracing for high-tech companies. To this end, in this paper, we develop a novel data-driven solution, i.e., Deep Technology Forecasting (DTF) framework, to automatically find the most possible technology directions customized to each high-tech company. Specially, DTF consists of three components: Potential Competitor Recognition (PCR), Collaborative Technology Recognition (CTR), and Deep Technology Tracing (DTT) neural network. For one thing, PCR and CTR aim to capture competitive relations among enterprises and collaborative relations among technologies, respectively. For another, DTT is designed for modeling dynamic interactions between companies and technologies with the above relations involved. Finally, we evaluate our DTF framework on real-world patent data, and the experimental results clearly prove that DTF can precisely help to prospect future technology emphasis of companies by exploiting hybrid factors.

How to identify high-potential talent (HIPO) earlier in their career always has strategic importance for human resource management. While tremendous efforts have been made in this direction, most existing approaches are still based on the subjective selection of human resource experts. This could lead to unintentional bias and inconsistencies. To this end, in this paper, we propose a neural network based dynamic social profiling approach for quantitatively identifying HIPOs from the newly-enrolled employees by modeling the dynamics of their behaviors in organizational social networks. A basic assumption is that HIPOs usually perform more actively and have higher competencies than their peers to accumulate their social capitals during their daily work practice. Along this line, we first propose to model the social profiles of employees with both Graph Convolutional Network (GCN) and social centrality analysis in a comprehensive way. Then, an adaptive Long Short Term Memory (LSTM) network with global attention mechanism is designed to capture the profile dynamics of employees in the organizational social networks during their early career. Finally, extensive experiments on real-world data clearly validate the effectiveness of our approach as well as the interpretability of our results.

As users implicitly express their preferences to items on many real-world applications, the implicit feedback based collaborative filtering has attracted much attention in recent years. Pairwise methods have shown state-of-the-art solutions for dealing with the implicit feedback, with the assumption that users prefer the observed items to the unobserved items. However, for each user, the huge unobserved items are not equal to represent her preference. In this paper, we propose a Multiple Pairwise Ranking (MPR) approach, which relaxes the simple pairwise preference assumption in previous works by further tapping the connections among items with multiple pairwise ranking criteria. Specifically, we exploit the preference difference among multiple pairs of items by dividing the unobserved items into different parts. Empirical studies show that our algorithms outperform the state-of-the-art methods on real-world datasets.