Multimodal recommendation enhances accuracy by leveraging visual and textual signals, and its success largely depends on learning high-quality cross-modal representations. Recent advances in Large Vision-Language Models (LVLMs) offer unified multimodal representation learning, making them a promising backbone. However, applying LVLMs to recommendation remains challenging due to ( i ) representation misalignment, where gaps between specific domain data and general pre-training lead to unaligned embedding spaces, and ( ii ) gradient conflicts during fine-tuning, where shared adapters cause interference and a lack of discriminative power. To address this, we propose SDA, a lightweight framework for Structural and Disentangled Adaptation, which integrates two components: Cross-Modal Structural Alignment (CMSA) and Modality-Disentangled Adaptation (MoDA). CMSA aligns embeddings using intra-modal structures as a soft teacher, while MoDA mitigates gradient conflicts via expertized, gated low-rank paths to disentangle gradient flows. Experiments on three public Amazon datasets show SDA integrates seamlessly with existing multimodal and sequential recommenders, yielding average gains of 6.15% in Hit@10 and 8.64% in NDCG@10. It also achieves up to 12.83% and 18.70% gains on long-tail items. Our code and full experimental results are available at https://github.com/RaoZhongtao/SDA.

Inspired by advances in LLMs, reasoning-enhanced sequential recommendation performs multi-step deliberation before making final predictions, unlocking greater potential for capturing user preferences. However, current methods are constrained by static reasoning trajectories that are ill-suited for the diverse complexity of user behaviors. They suffer from two key limitations: (1) a static reasoning direction, which uses flat supervision signals misaligned with human-like hierarchical reasoning, and (2) a fixed reasoning depth, which inefficiently applies the same computational effort to all users, regardless of pattern complexity. These rigidity lead to suboptimal performance and significant computational waste. To overcome these challenges, we propose DTRec, a novel and effective framework that explores the Dynamic reasoning Trajectory for Sequential Recommendation along both direction and depth. To guide the direction, we develop Hierarchical Process Supervision (HPS), which provides coarse-to-fine supervisory signals to emulate the natural, progressive refinement of human cognitive processes. To optimize the depth, we introduce the Adaptive Reasoning Halting (ARH) mechanism that dynamically adjusts the number of reasoning steps by jointly monitoring three indicators. Extensive experiments on three real-world datasets demonstrate the superiority of our approach, achieving up to a 24.5% performance improvement over strong baselines while simultaneously reducing computational cost by up to 41.6%.

Deep reinforcement learning (DRL), has shown promise in solving intractable challenges in interactive recommendation systems (IRS). In DRL-based interactive recommendation, state modeling is vital for well-capturing users’ continuous interaction behaviors with recommendation systems. To effectively capture the behavior of users, existing works for state modeling have evolved from sequential-based modeling to session-based modeling. However, existing session-based state modeling works in IRS are still not fully explored with premature session models and insufficient fusion for different session features. As a result, they cannot capture complicated session patterns during interaction, leading to significant information loss. In this article, we propose a Knowledge-enhanced Multi-Level Session Graph (KMSG) model for interactive recommendation to address the above challenge. KMSG models the user’s interactive data into multi-level session graphs and effectively encodes the states via graph neural networks. Specifically, a novel 3-level item transition graph is designed to capture the common session patterns and intra-session item transitions. We further utilize the information from the knowledge graph to enhance the item relations in KMSG. We then design an attention-based graph neural network to propagate the information in KMSG. Extensive experiments on four real-world benchmark datasets demonstrate the superiority of KMSG over state-of-the-art baselines and the rationality of our design in KMSG.

Content recommender systems (RSs), which recommend content (e.g., news, videos) to users, can inadvertently facilitate the spread of misinformation (e.g., fake news, inaccurate content) due to their ignorance of content credibility. The widespread misinformation can lead to serious consequences, including public confusion and social unrest. Despite the pressing need to mitigate misinformation in content RSs, only a few studies have attempted to tackle this. Moreover, they generally require retraining the entire model from scratch, which is time-consuming and impractical for real-world applications. To this end, we propose MisEraser , a novel misinformation unlearning framework that effectively mitigates misinformation in content RSs without retraining. Specifically, MisEraser consists of three key components: (1) A misinformation disentanglement network , which effectively separates misinformation-specific information from item content while preserving misinformation-free information to maintain recommendation accuracy; (2) A misinformation fusion network , which fuses misinformation-specific signals from both users’ interaction sequences and the entire misinformation item set to enhance the comprehensiveness of misinformation unlearning; and (3) A misinformation erasing network , which effectively removes the fused misinformation-specific signals from trained recommendation models, enabling them to generate misinformation-suppressed and more responsible recommendations. Extensive experiments demonstrate the effectiveness of MisEraser in mitigating misinformation while maintaining high recommendation accuracy and efficiency.

Deep Reinforcement Learning (DRL) has garnered significant attention as a promising approach for developing intelligent and adaptive recommender systems. This paradigm is especially well-suited for recommendation scenarios characterized by dynamic user environments, temporally delayed feedback signals, and continuously shifting user preferences. However, deploying DRL within recommendation scenarios introduces a range of intricate challenges. These include the design of meaningful and task-aligned reward functions, effective navigation of vast and complex action spaces, and the need to maintain sample efficiency in data-sparse environments. Ensuring robust and stable training dynamics adds further difficulty. This special issue brings together a diverse collection of cutting-edge research that addresses these pressing challenges, showcasing advances that move the field toward more adaptive, robust, and personalized recommendation systems grounded in reinforcement learning.

Recently, frequency transformation (FT) has been increasingly incorporated into deep learning models to significantly enhance state-of-the-art accuracy and efficiency in time series analysis. The advantages of FT, such as high efficiency and a global view, have been rapidly explored and exploited in various time series tasks and applications, demonstrating the promising potential of FT as a new deep learning paradigm for time series analysis. Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT. It is also unclear why FT can enhance time series analysis and what its limitations are in the field. To address these gaps, we present a comprehensive review that systematically investigates and summarizes the recent research advancements in deep learning-based time series analysis with FT. Specifically, we explore the primary approaches used in current models that incorporate FT, the types of neural networks that leverage FT, and the representative FT-equipped models in deep time series analysis. We propose a novel taxonomy to categorize the existing methods in this field, providing a structured overview of the diverse approaches employed in incorporating FT into deep learning models for time series analysis. Finally, we highlight the advantages and limitations of FT for time series modeling and identify potential future research directions that can further contribute to the community of time series analysis.

Sequential recommender systems (SRSs) aim to recommend the next items to well match users' preferences. In addition to recommendation accuracy, diversity is another critical aspect in evaluating SRSs. Recently, the emerging diffusion models (DMs) have been widely adopted in SRSs. Their employed learning-to-generate paradigm allows them to cover a much broader range of users' preferences and thus generate more diversified items. However, existing DM-based SRSs still face two significant gaps that prevent them from further improving the recommendation diversity: (1) they often rely on non-diversified users' preferences as guidance to direct the training of diffusion networks, restricting networks' ability to generate diverse items; and (2) they are based on a homogeneous diffusion inference mechanism to generate the next items and thus can only accommodate users' major preferences. Such a practice neglects users' heterogeneous preferences towards various types of items, further limiting recommendation diversity. To bridge these two critical gaps and to further unleash the potential of DMs in enhancing the recommendation diversity of SRSs, we propose a novel diversity-guided diffusion model for sequential recommendations, called DiffDiv for short. To be specific, first, a new diversity-aware guidance learning mechanism is devised to direct the training of DMs to effectively capture users' diversified preferences from their historical interactions. Then, a novel heterogeneous diffusion inference mechanism is designed to generate diversified items to accommodate users' heterogeneous preferences, further boosting the recommendation diversity. Extensive experiments on real-world datasets validate the effectiveness of DiffDiv in terms of both recommendation accuracy and diversity.

Incomplete scenario is a prevalent, practical, yet challenging setting in Multimodal Recommendations (MMRec), where some item modalities are missing due to various factors. Recently, a few efforts have sought to improve the recommendation accuracy by exploring generic structures from incomplete data. However, two significant gaps persist: 1) the difficulty in accurately generating missing data due to the limited ability to capture modality distributions; and 2) the critical but overlooked visibility bias, where items with missing modalities are more likely to be disregarded due to the prioritization of items' multimodal data over user preference alignment. This bias raises serious concerns about the fair treatment of items. To bridge these two gaps, we propose a novel Modality-Diffused Counterfactual (MoDiCF) framework for incomplete multimodal recommendations. MoDiCF features two key modules: a novel modality-diffused data completion module and a new counterfactual multimodal recommendation module. The former, equipped with a particularly designed multimodal generative framework, accurately generates and iteratively refines missing data from learned modality-specific distribution spaces. The latter, grounded in the causal perspective, effectively mitigates the negative causal effects of visibility bias and thus assures fairness in recommendations. Both modules work collaboratively to address the two aforementioned significant gaps for generating more accurate and fair results. Extensive experiments on three real-world datasets demonstrate the superior performance of MoDiCF in terms of both recommendation accuracy and fairness. The code and processed datasets are released at https://github.com/JinLi-i/MoDiCF.

User-generated content on social media platforms provides a valuable resource for developing automated computational methods to detect mental health issues online leading to suicidal thoughts automatically. Although current fully automated methods show promise, they may produce uncertain predictions, leading to flawed conclusions. To address this, we propose a novel model called DiGrI, or Distorted Greedy Approach for Human-Assisted Online Suicide Ideation Detection, which reformulates suicide ideation assessment as a selective, prioritized prediction problem. The model incorporates a novel multi-classifier distorted greedy model that is optimized to operate under various levels of automation and abstains from making uncertain predictions with theoretical guarantees. Our results show that DiGrI outperforms strong comparative models including large language models in detecting mental health issues on a publicly available Reddit dataset. We discuss the empirical and practical implications, including the ethical considerations of using DiGrI for online automatic suicide ideation detection involving humans, if it were to be translated for use in clinical and public health practice.

Cross-domain fake news detection aims to mitigate domain shift and improve detection performance by transferring knowledge across domains. Existing approaches transfer knowledge based on news content and user engagements from a source domain to a target domain. However, these approaches face two main limitations, hindering effective knowledge transfer and optimal fake news detection performance. Firstly, from a micro perspective, they neglect the negative impact of veracity-irrelevant features in news content when transferring domain-shared features across domains. Secondly, from a macro perspective, existing approaches ignore the relationship between user engagement and news content, which reveals shared behaviors of common users across domains and can facilitate more effective knowledge transfer. To address these limitations, we propose a novel macro- and micro- hierarchical transfer learning framework (MMHT) for cross-domain fake news detection. Firstly, we propose a micro-hierarchical disentangling module to disentangle veracity-relevant and veracity-irrelevant features from news content in the source domain for improving fake news detection performance in the target domain. Secondly, we propose a macro-hierarchical transfer learning module to generate engagement features based on common users' shared behaviors in different domains for improving effectiveness of knowledge transfer. Extensive experiments on real-world datasets demonstrate that our framework significantly outperforms the state-of-the-art baselines.

As multimedia content continues to grow on the web, the integration of visual and textual data has become a crucial challenge for web applications, particularly in recommendation systems. Large Vision Language Models (LVLMs) have demonstrated considerable potential in addressing this challenge across various tasks that require such multimodal integration. However, their application in multimodal sequential recommendation (MSR) has not been extensively studied. To bridge this gap, we introduce MSRBench, the first comprehensive benchmark designed to systematically evaluate different LVLM integration strategies in web-based recommendation scenarios. We benchmark three state-of-the-art LVLMs, i.e., GPT-4 Vision, GPT-4o, and Claude-3-Opus, on the next item prediction task using the constructed Amazon Review Plus dataset, which includes additional item descriptions generated by LVLMs. Our evaluation examines five integration strategies: using LVLMs as recommender, item enhancer, reranker, and various combinations of these roles. The benchmark results reveal that 1) using LVLMs as rerankers is the most effective strategy, significantly outperforming others that rely on LVLMs to directly generate recommendations or only enhance items; 2) GPT-4o consistently achieves the best performance across most scenarios, particularly when employed as a reranker; 3) the computational inefficiency of LVLMs presents a major barrier to their widespread adoption in real-time multimodal recommendation systems. Our code and datasets are available at https://github.com/PALIN2018/MSRBench.

Session-based Recommender Systems (SBRSs) aim to recommend the next item to users based on their historical interactions with items within or between sessions. A session is constituted by a sequence of interactions between the user and items within a continuous period. Existing SBRSs often focus on modeling co-occurrence-based inter-item transitions within or between sessions only. They generally overlook intrinsic inter-item semantic relations. Specifically, in practice, many items are substitutable or complementary to each other. Such relations provide significant signals to guide user interaction behaviors as well as the next-item recommendations. Moreover, existing works overlook the fact that user behaviors are driven simultaneously by both user intent and item attributes, failing to consider the implicit item characteristics embedded within. Such practice leads to entangled user intent and latent item characteristics, bringing unnecessary interference between these two aspects, impeding accurate modeling of each aspect, ultimately significantly impeding recommendation performance. To bridge these gaps, we propose a novel framework called S emantic relation guided dual-view C ontrastive L earning for S ession-based R ecommendations (SCL-SR). SCL-SR introduces a novel semantic relation-guided contrastive learning module to capture additional supervision signals from both user intent view and item attribute view to guide the next-item prediction better. Then, we propose a novel intent-attribute disentangler to effectively mitigate the interference between user intent and latent item characteristics for further improving the recommendation performance. Extensive experiments on three real-world datasets demonstrate the significant superiority of SCL-SR over the state-of-the-art approaches, including achieving substantial improvements ranging from 7.10% to 12.82% on the Tmall dataset. Our source code and datasets are available at https://github.com/Nishikata97/SCL-SR .

Automatic Chinese patent approval prediction is an emerging and valuable task in patent analysis. However, it involves a rigorous and transparent decision-making process that includes patent comparison and examination to assess its innovation and correctness. This resultant necessity of decision evidentiality, coupled with intricate patent comprehension presents significant challenges and obstacles for the patent analysis community. Consequently, few existing studies are addressing this task. This paper presents the pioneering effort on this task using a retrieval-based classification approach. We propose a novel framework called DiSPat, which focuses on structural representation learning and disentanglement to predict the approval of Chinese patents and offer decision-making evidence. DiSPat comprises three main components: base reference retrieval to retrieve the Top-k most similar patents as a reference base; structural patent representation to exploit the inherent claim hierarchy in patents for learning a structural patent representation; disentangled representation learning to learn disentangled patent representations that enable the establishment of an evidential decision-making process. To ensure a thorough evaluation, we have meticulously constructed three datasets of Chinese patents. Extensive experiments on these datasets unequivocally demonstrate our DiSPat surpasses state-of-the-art baselines on patent approval prediction, while also exhibiting enhanced evidentiality.

The inherent complexity of real-world time series data, combined with the cost and infeasibility of manual labeling, presents considerable challenges to time series representation learning. Most existing studies tend to utilize data augmentation techniques to construct positive and negative samples and leverage a comparative learning framework to generate time series representations. However, they typically employ simple data augmentation techniques, such as jitter and cropping, to construct positive samples while randomly selecting irrelevant samples as negative ones, which are easily distinguished and unable to guide comparative learning to capture subtle discriminative features. Furthermore, they usually employ only a single positive sample for comparative learning, which is insufficient to model the diversity and hurts the robustness. To address these issues, this paper proposes a Time Series representation learning framework via Dual Reference Contrasting (TS-DRC). Specifically, we first utilize Markov transition field or Gramian angular field to transform the anchor sample of time series into image representations, which are adopted as positive samples. Then, we incorporate two positive samples (dual references) and one negative sample into the comparative learning framework, and devise a novel optimization objective to guide the model to capture more discriminate features, mitigate overfitting, and enhance the robustness. Extensive experiments conducted on four public real-world datasets demonstrate that our TS-DRC outperforms other state-of-the-art baselines.Our code is available at: https://github.com/yurui12138/TS-DRC.

Transfer of knowledge across domains is the focus for cross-domain and multi-domain fake news detection. However, most of the existing methods based on cross-domain knowledge transfer have two issues: (1) they usually ignore domain-specific features; (2) they are less effective in handling the imbalanced data distribution across domains. Targeting these two issues, we focus on how to effectively leverage user-news engagements in both data-richer and data-sparser domains. This is because not only users' engagement characteristics closely relate to the veracity of the engaged news, but also there are consistent patterns in common users' engagements with news across domains. Considering these two insights, this work aims to perform dual-target cross-domain fake news detection via well modeling users' engagement patterns. In particular, it aims to transfer knowledge based on user-news engagements for handling the imbalanced data distribution across domains, which is novel but challenging. To this end, in this paper, we propose a novel framework to mine User-news engagement Patterns for DuAl-TargEt cross-domain fake news detection (UPDATE). In UPDATE, we first mine user-news engagement patterns as the key auxiliary information for cross-domain knowledge transfer. In such a way, it avoids the necessity to remove the domain-specific news information, and thereby, better preserve useful news information. Then, we combine engagement features of common users in both data-richer and data-sparser domains. By doing so, UPDATE improves the information richness in each of the two domains, thus improving detection performance in both domains when detecting news from domains with imbalanced data distribution. Extensive experiments conducted on real-world datasets demonstrate that UPDATE significantly outperforms state-of-the-art cross-domain and multi-domain methods as well as large language models (LLMs), such as GPT-3.5-turbo in terms of AUC and Fl-score for fake news detection.

In the era of information explosion, news recommender systems are crucial for users to effectively and efficiently discover their interested news. However, most of the existing news recommender systems face two major issues, hampering recommendation quality. Firstly, they often oversimplify users' reading interests, neglecting their hierarchical nature, spanning from high-level event (e.g., US Election) related interests to low-level news article-specifc interests. Secondly, existing work often assumes a simplistic context, disregarding the prevalence of fake news and political bias under the real-world context. This oversight leads to recommendations of biased or fake news, posing risks to individuals and society. To this end, this paper addresses these gaps by introducing a novel framework, the Hierarchical and Disentangling Interest learning framework (HDInt). HDInt incorporates a hierarchical interest learning module and a disentangling interest learning module. The former captures users' high- and low-level interests, enhancing next-news recommendation accuracy. The latter effectively separates polarity and veracity information from news contents and model them more specifcally, promoting fairness- and truth-aware reading interest learning for unbiased and true news recommendations. Extensive experiments on two real-world datasets demonstrate HDInt's superiority over state-of-the-art news recommender systems in delivering accurate, unbiased, and true news recommendations.

The proliferation of social media platforms has fueled the rapid dissemination of fake news, posing threats to our real-life society. Existing methods use multimodal data or contextual information to enhance the detection of fake news by analyzing news content and/or its social context. However, these methods often overlook essential textual news content (articles) and heavily rely on sequential modeling and global attention to extract semantic information. These existing methods fail to handle the complex, subtle twists1 in news articles, such as syntax-semantics mismatches and prior biases, leading to lower performance and potential failure when modalities or social context are missing. To bridge these significant gaps, we propose a novel multi-hop syntax aware fake news detection (MSynFD) method, which incorporates complementary syntax information to deal with subtle twists in fake news. Specifically, we introduce a syntactical dependency graph and design a multi-hop subgraph aggregation mechanism to capture multi-hop syntax. It extends the effect of word perception, leading to effective noise filtering and adjacent relation enhancement. Subsequently, a sequential relative position-aware Transformer is designed to capture the sequential information, together with an elaborate keyword debiasing module to mitigate the prior bias. Extensive experimental results on two public benchmark datasets verify the effectiveness and superior performance of our proposed MSynFD over state-of-the-art detection models.

Sequential recommender systems (SRS) are designed to predict users' future behaviors based on their historical interaction data. Recent research has increasingly utilized contrastive learning (CL) to leverage unsupervised signals to alleviate the data sparsity issue in SRS. In general, CL-based SRS first augments the raw sequential interaction data by using data augmentation strategies and employs a contrastive training scheme to enforce the representations of those sequences from the same raw interaction data to be similar. Despite the growing popularity of CL, data augmentation, as a basic component of CL, has not received sufficient attention. This raises the question: Is it possible to achieve superior recommendation results solely through data augmentation? To answer this question, we benchmark eight widely used data augmentation strategies, as well as state-of-the-art CL-based SRS methods, on four real-world datasets under both warm- and cold-start settings. Intriguingly, the conclusion drawn from our study is that, certain data augmentation strategies can achieve similar or even superior performance compared with some CL-based methods, demonstrating the potential to significantly alleviate the data sparsity issue with fewer computational overhead. We hope that our study can further inspire more fundamental studies on the key functional components of complex CL techniques. Our processed datasets and codes are available at https://github.com/AIM-SE/DA4Rec.

Recommender systems (RSs) aim at helping users to effectively retrieve items of their interests from a large catalogue. For a quite long time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, and various types of biases. As a result, it has become clear that the focus on RS accuracy is too narrow, and the research must consider other important factors, particularly trustworthiness. A trustworthy recommender system (TRS) should not only be accurate but also transparent, unbiased, fair, and robust to noise and attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, there is a lack of a systematic overview and discussion of the literature in this novel and fast-developing field of TRSs. To this end, in this article, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area. We also provide a novel conceptual framework to support the construction of TRSs.

Multivariate time series (MTS) forecasting penetrates various aspects of our economy and society, whose roles become increasingly recognized. However, often MTS forecasting is unfair, not only degrading their practical benefits but even incurring potential risk. Unfair MTS forecasting may be attributed to disparities relating to advantaged and disadvantaged variables, which has rarely been studied in the MTS forecasting. In this work, we formulate the MTS fairness modeling problem as learning informative representations attending to both advantaged and disadvantaged variables. Accordingly, we propose a novel framework, namedFairFor, for fairness-aware MTS forecasting, i.e.,fair MTS forecasting.FairForuses adversarial learning to generate both group-irrelevant and -relevant representations for downstream forecasting.FairForfirst adopts recurrent graph convolution to capture spatio-temporal variable correlations and to group variables by leveraging a spectral relaxation of the K-means objective. Then, it utilizes a novel filtering$\&$fusion module to filter group-relevant information and generate group-irrelevant representations by orthogonality regularization. The group-irrelevant and -relevant representations form highly informative representations, facilitating to share the knowledge from advantaged variables to disadvantaged variables and guarantee the fairness of forecasting. Extensive experiments on four public datasets demonstrate theFairForeffectiveness for fair forecasting and significant performance improvement.

Transformer-based sequential recommendation (SR) has been booming in recent years, with the self-attention mechanism as its key component. Self-attention has been widely believed to be able to effectively select those informative and relevant items from a sequence of interacted items for next-item prediction via learning larger attention weights for these items. However, this may not always be true in reality. Our empirical analysis of some representative Transformer-based SR models reveals that it is not uncommon for large attention weights to be assigned to less relevant items, which can result in inaccurate recommendations. Through further in-depth analysis, we find two factors that may contribute to such inaccurate assignment of attention weights:sub-optimal position encoding andnoisy input. To this end, in this paper, we aim to address this significant yet challenging gap in existing works. To be specific, we propose a simple yet effective framework called Attention Calibration for Transformer-based Sequential Recommendation (AC-TSR). In AC-TSR, a novel spatial calibrator and adversarial calibrator are designed respectively to directly calibrates those incorrectly assigned attention weights. The former is devised to explicitly capture the spatial relationships (i.e., order and distance) among items for more precise calculation of attention weights. The latter aims to redistribute the attention weights based on each item's contribution to the next-item prediction. AC-TSR is readily adaptable and can be seamlessly integrated into various existing transformer-based SR models. Extensive experimental results on four benchmark real-world datasets demonstrate the superiority of our proposed AC-TSR via significant recommendation performance enhancements. The source code is available at https://github.com/AIM-SE/AC-TSR.

Deep reinforcement learning (DRL), has shown promise in solving intractable challenges in interactive recommendation systems. In DRL-based interactive recommendation, state modeling is crucial for well-capturing users’ continuous interaction behaviors with shopping systems. A user’s multiple continuous interactions in a given time period (e.g., the time from login to log out) naturally constitute a session. However, existing studies often overlook such valuable session structure and characteristics and instead simply treat them as sequences. As a result, they are not able to capture the complex transitions over users’ interactions within or between sessions, leading to significant information loss. To bridge this significant gap, in this paper, we propose Session-based Interactive Recommendation with Graph Neural Networks (SIR-GNN). SIR-GNN models interaction data as sessions and employs novel graph neural networks to capture rich transition patterns among interactions. Specifically, a novel 3-level transition module is well designed to effectively capture common patterns from all sessions, intra-session transitions, and adjacent-item transitions respectively, followed by an attention-based gated graph neural network to model the state representation for SIR well. Extensive experiments on 3 real-world benchmark datasets demonstrate the superiority of SIR-GNN over state-of-the-art baselines and the rationality of our design in SIR-GNN.

Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augmentation strategies to generate positive pairs and encourage their representations to be invariant. However, due to the inherent properties of user behavior sequences, some augmentation strategies, such as item substitution, can lead to changes in user intent. Learning indiscriminately invariant representations for all augmentation strategies might be sub-optimal. Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e.g., item substitution) and insensitive to mild augmentations (e.g., feature-level dropout masking). In detail, we use the conditional discriminator to capture differences in behavior due to item substitution, which encourages the user behavior encoder to be equivariant to invasive augmentations. Comprehensive experiments on four benchmark datasets show that the proposed ECL-SR framework achieves competitive performance compared to state-of-the-art SR models. The source code is available at https://github.com/Tokkiu/ECL.

Sequential recommender systems (SRSs) aim to predict the subsequent items which may interest users via comprehensively modeling users' complex preference embedded in the sequence of user-item interactions. However, most of existing SRSs often model users' single low-level preference based on item ID information while ignoring the high-level preference revealed by item attribute information, such as item category. Furthermore, they often utilize limited sequence context information to predict the next item while overlooking richer inter-item semantic relations. To this end, in this paper, we proposed a novel hierarchical preference modeling framework to substantially model the complex low- and high-level preference dynamics for accurate sequential recommendation. Specifically, in the framework, a novel dual-transformer module and a novel dual contrastive learning scheme have been designed to discriminatively learn users' low- and high-level preference and to effectively enhance both low- and high-level preference learning respectively. In addition, a novel semantics-enhanced context embedding module has been devised to generate more informative context embedding for further improving the recommendation performance. Extensive experiments on six real-world datasets have demonstrated both the superiority of our proposed method over the state-of-the-art ones and the rationality of our design.

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

Sequential recommendation aims to predict the next item which interests users via modeling their interest in items over time. Most of the existing works on sequential recommendation model users’ dynamic interest in specific items while overlooking users’ static interest revealed by some static attribute information of items, e.g., category, brand. Moreover, existing works often only consider the positive excitation of a user’s historical interactions on his/her next choice on candidate items while ignoring the commonly existing negative excitation, resulting in insufficiently modeling dynamic interest. The overlook of static interest and negative excitation will lead to incomplete interest modeling and thus impedes the recommendation performance. To this end, in this paper, we propose modeling both static interest and negative excitation for dynamic interest to further improve the recommendation performance. Accordingly, we design a novel Static-Dynamic Interest Learning (SDIL) framework featured with a novel Temporal Positive and Negative Excitation Modeling (TPNE) module for accurate sequential recommendation. TPNE is specially designed for comprehensively modeling dynamic interest based on temporal positive and negative excitation learning. Extensive experiments on three real-world datasets show that SDIL can effectively capture both static and dynamic interest and outperforms state-of-the-art baselines.

Most session-based recommender systems (SBRSs) focus on extracting information from the observed items in the current session of a user to predict a next item, ignoring the causes outside the session (called outer-session causes, OSCs) that influence the user’s selection of items. However, these causes widely exist in the real world, and few studies have investigated their role in SBRSs. In this work, we analyze the causalities and correlations of the OSCs in SBRSs from the perspective of causal inference. We find that the OSCs are essentially the confounders in SBRSs, which leads to spurious correlations in the data used to train SBRS models. To address this problem, we propose a novel SBRS framework named COCO-SBRS (COunterfactual COllaborative Session-Based Recommender Systems) to learn the causality between OSCs and user-item interactions in SBRSs. COCO-SBRS first adopts a self-supervised approach to pre-train a recommendation model by designing pseudo-labels of causes for each user’s selection of the item in data to guide the training process. Next, COCO-SBRS adopts counterfactual inference to recommend items based on the outputs of the pre-trained recommendation model considering the causalities to alleviate the data sparsity problem. As a result, COCO-SBRS can learn the causalities in data, preventing the model from learning spurious correlations. The experimental results of our extensive experiments conducted on three real-world datasets demonstrate the superiority of our proposed framework over ten representative SBRSs.

Streaming Recommender Systems (SRSs) have emerged to deliver recommendations based on pervasive data streams, which are a sequence of user-item interactions with multiple behavior types (e.g., purchase, add-to-cart, and view). However, existing SRSs all rely on a single behavior type (e.g., purchase) to make streaming recommendations, and commonly suffer from the data sparsity problem. To address this issue, the relatively more abundant multi-behavior interactions (i.e., interactions with multiple behavior types) could be well leveraged for more accurate streaming recommendations. However, it remains a challenge on how to effectively leverage the commonly-existing and complex multi-behavior interactions for improving the accuracy of streaming recommendations. Targeting at this challenge, we propose the first Multi-behavior Streaming Recommender System in the literature, called MbSRS, to elaborately exploit multi-behavior interactions for delivering accurate recommendations in streaming scenarios. In MbSRS, we first learn instant user preferences and unified item characteristics collaboratively from multi-behavior interactions. Then, we attentively learn long-term user preferences from the historical items interacted by the corresponding users. After that, we wisely fuse the learned instant and long-term user preferences via a gate mechanism. Finally, a novel multi-behavior-specific training process is devised for more effectively learning user preferences towards items from multi-behavior interactions. Extensive experiments on three real-world datasets demonstrate that the proposed MbSRS significantly outperforms the state-of-the-art baselines.

Users’ purchase behaviors are complex and dynamic, which are usually driven by various personal demands evolving with time. According to psychology and economic theories, user demands can be satisfied with a sequence of purchase behaviors, resulting in a basket of items. However, most of the existing works simply predict the next basket from a shallow perspective of (purchase) sequence data modeling without deep insight into the underlying factors which drive user purchase behaviors. In fact, filling a basket with multiple items is a process to incrementally satisfy a user's demand. Therefore, the key challenges to predict a user's next basket lie in (1) how to track the changes of the user's demand, and (2) how to satisfy her demand at a given moment. To this end, we propose an Evolving DEmand SAtisfaction (EvoDESA) model to model a user's demand evolution for next-basket prediction. In EvoDESA, a demand evolution module learns the dynamics of user demand over a sequence of basket-purchase behaviors. Then, a next-basket planning module effectively packs an optimal combination of items to best satisfy the user's current demand. Extensive experiments on three real-world transaction datasets demonstrate the considerable superiority of EvoDESA over the state-of-the-art approaches.

Next basket recommender systems (NBRs) aim to recommend a user’s next (shopping) basket of items via modeling the user’s preferences towards items based on the user’s purchase history, usually a sequence of historical baskets. Due to its wide applicability in the real-world E-commerce industry, the studies NBR have attracted increasing attention in recent years. NBRs have been widely studied and much progress has been achieved in this area with a variety of NBR approaches having been proposed. However, an important issue is that there is a lack of a systematic and unified evaluation over the various NBR approaches. Different studies often evaluate NBR approaches on different datasets, under different experimental settings, making it hard to fairly and effectively compare the performance of different NBR approaches. To bridge this gap, in this work, we conduct a systematical empirical study in NBR area. Specifically, we review the representative work in NBR and analyze their cons and pros. Then, we run the selected NBR algorithms on the same datasets, under the same experimental setting and evaluate their performances using the same measurements. This provides a unified framework to fairly compare different NBR approaches. We hope this study can provide a valuable reference for the future research in this vibrant area.

With the advancement of data science and AI, more and more powerful and accurate recommender systems (RSs) have been developed. They provide recommendation services in various areas, including shopping, eating, travelling and entertainment. RSs have achieved a great success and benefted the society. However, most of the research on RS has focused on the improvement of the recommendation accuracy, while ignoring other important qualities, such as trustworthiness (robustness, fairness, explainability, privacy and security) and social impact (influence on users' recognition and behaviours) of the recommendations. These are important aspects and cannot be overlooked since they measure properties that determine whether the recommendation service is reliable, trustworthy and benefcial to individual users and society. In this work, responsible recommendations refer to trustworthy recommendation techniques and positive-social-impact recommendation results.

In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations. Although SRSs and SBRSs have been extensively studied, there are many inconsistencies in this area caused by the diverse descriptions, settings, assumptions and application domains. There is no work to provide a unified framework and problem statement to remove the commonly existing and various inconsistencies in the area of SR/SBR. There is a lack of work to provide a comprehensive and systematic demonstration of the data characteristics, key challenges, most representative and state-of-the-art approaches, typical real- world applications and important future research directions in the area. This work aims to fill in these gaps so as to facilitate further research in this exciting and vibrant area.

Despite the tremendous efforts by social media platforms and fact-check services for fake news detection, fake news and misinformation still spread wildly on social media platforms (e.g., Twitter). Consequently, fake news mitigation strategies are urgently needed. Most of the existing work on fake news mitigation focuses on the overall mitigation on a whole social network while ignoring developing concrete mitigation strategies to deter individual users from sharing fake news. In this paper, we propose a novel veracity-aware and event-driven recommendation model to recommend personalised corrective true news to individual users for effectively debunking fake news. Our proposed model Rec4Mit (Recommendation for Mitigation) not only effectively captures a user’s current reading preference with a focus on which event, e.g., US election, from her/his recent reading history containing true and/or fake news, but also accurately predicts the veracity (true or fake) of candidate news. As a result, Rec4Mit can recommend the most suitable true news to best match the user’s preference as well as to mitigate fake news. In particular, for those users who have read fake news of a certain event, Rec4Mit is able to recommend the corresponding true news of the same event. Extensive experiments on real-world datasets show Rec4Mit significantly outperforms the state-of-the-art news recommendation methods in terms of the capability to recommend personalized true news for fake news mitigation.

Session-based recommendations (SBR) play an important role in many real-world applications, such as e-commerce and media streaming. To perform accurate session-based recommendations, it is crucial to capture both sequential dependencies over a sequence of adjacent items and complex item transitions over a set of items within sessions. Note that item transitions are not necessarily dependent on sequential dependencies, e.g., the transition from one item to the other distant item in a session is often not sequential. However, almost all the existing session-based recommender systems (SBRS) fail to consider both kinds of information, which leads to their limited performance improvement. Aiming at this deficiency, we propose a novel sequential dependency enhanced graph neural network (SDE-GNN) to capture both sequential dependencies and item transition relations over items within sessions for more accurate next-item recommendations. Specifically, we first devise a sequential dependency learning module to capture the sequential dependencies over a sequence of adjacent items in each session. Then, we propose an item transition learning module to capture complex transitions between items. In the module, a novel residual gate and a specialized attention mechanism are integrated into gate-GNN to build an attention augmented GNN, called AU-GNN. Finally, we devise a gated fusion component to combine the learned sequential dependencies and item transitions together in preparation for the subsequent next-item recommendations. Exhaustive experiments on two public real-world data sets demonstrate the superiority of SDE-GNN over the state-of-the-art methods.

The changing preferences of users towards items trigger the emergence of session-based recommender systems (SBRSs), which aim to model the dynamic preferences of users for next-item recommendations. However, most of the existing studies on SBRSs are based on long sessions only for recommendations, ignoring short sessions, though short sessions, in fact, account for a large proportion in most of the real-world datasets. As a result, the applicability of existing SBRSs solutions is greatly reduced. In a short session, quite limited contextual information is available, making the next-item recommendation very challenging. To this end, in this paper, inspired by the success of few-shot learning (FSL) in effectively learning a model with limited instances, we formulate the next-item recommendation as an FSL problem. Accordingly, following the basic idea of a representative approach for FSL, i.e., meta-learning, we devise an effective SBRS called INter-SEssion collaborativeRecommender neTwork (INSERT) for next-item recommendations in short sessions. With the carefully devised local module and global module, INSERT is able to learn an optimal preference representation of the current user in a given short session. In particular, in the global module, a similar session retrieval network (SSRN) is designed to find out the sessions similar to the current short session from the historical sessions of both the current user and other users, respectively. The obtained similar sessions are then utilized to complement and optimize the preference representation learned from the current short session by the local module for more accurate next-item recommendations in this short session. Extensive experiments conducted on two real-world datasets demonstrate the superiority of our proposed INSERT over the state-of-the-art SBRSs when making next-item recommendations in short sessions.

Currently, the generative adversarial network (GAN), with complex training processes in the physical machine (PM), has achieved great priority in image generation , audio conversion, image translation, etc. To improve the training efficiency of GAN, the edge computing paradigm is accepted as an alternative of the PMs to accommodate the training tasks, that is, the training tasks are migrated to the edge nodes (ENs) for hosting. However, it is still a key challenge to keep the overall network performance (i.e., load balance , transmission time) and privacy protection of training tasks at the same time. To address this challenge, a privacy-aware task offloading method, named POM, is developed accordingly in this paper. First, improving the strength pareto evolutionary algorithm (SPEA2) is fully investigated to obtain the offloading strategies for collaboratively improving the training performance and privacy preservation . Then, the most balanced offloading strategy is acquired for training GAN. Eventually, systematic experiments indicate that POM achieves an optimal performance efficiently among the other representative benchmark methods.