361 citations · 635 across the 20 of their papers we have counts for
17 papers · 1 filter
Eval4DiRec: A Unified and Systematic Evaluation Framework for Diffusion-based Recommender Systems
Cong Wang, Shoujin Wang, Yishuo Li +3
Leveraging the strong generative capabilities and stable training dynamics of diffusion models, diffusion-based recommender systems (RSs) have recently emerged as a novel recommend…
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation
Chengkai Huang, Shoujin Wang, Xianzhi Wang +1
Sequential recommender systems (SRSs) aim to predict the subsequent items which may interest users via comprehensively modeling users' complex preference embedded in the sequence o…
Modeling Temporal Positive and Negative Excitation for Sequential Recommendation
Chengkai Huang, Shoujin Wang, Xianzhi Wang +1
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 recommendatio…
LLMRec: Benchmarking Large Language Models on Recommendation Task
Junling Liu, Chao Liu, Peilin Zhou +8
Recently, the fast development of Large Language Models (LLMs) such as ChatGPT has significantly advanced NLP tasks by enhancing the capabilities of conversational models. However,…
Attention Calibration for Transformer-based Sequential Recommendation
Peilin Zhou, Qichen Ye, Yueqi Xie +5
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 t…
A Counterfactual Collaborative Session-based Recommender System
Wenzhuo Song, Shoujin Wang, Yan Wang +3
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…
A Systematical Evaluation for Next-Basket Recommendation Algorithms
Zhufeng Shao, Shoujin Wang, Qian Zhang +3
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 hist…
Trustworthy Recommender Systems
Shoujin Wang, Xiuzhen Zhang, Yan Wang +2
Recommender systems (RSs) aim to help users to effectively retrieve items of their interests from a large catalogue. For a quite long period of time, researchers and practitioners…
Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities
Shoujin Wang, Qi Zhang, Liang Hu +3
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…
Aspect-driven User Preference and News Representation Learning for News Recommendation
Rongyao Wang, Wenpeng Lu, Shoujin Wang +3
News recommender systems are essential for helping users to efficiently and effectively find out those interesting news from a large amount of news. Most of existing news recommend…
Next-item Recommendations in Short Sessions
Wenzhuo Song, Shoujin Wang, Yan Wang +1
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-ite…
Graph Learning based Recommender Systems: A Review
Shoujin Wang, Liang Hu, Yan Wang +6
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph learning approaches to model u…
Double-Wing Mixture of Experts for Streaming Recommendations
Yan Zhao, Shoujin Wang, Yan Wang +2
Streaming Recommender Systems (SRSs) commonly train recommendation models on newly received data only to address user preference drift, i.e., the changing user preferences towards…
Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation
Shoujin Wang, Longbing Cao, Liang Hu +4
A transaction-based recommender system (TBRS) aims to predict the next item by modeling dependencies in transactional data. Generally, two kinds of dependencies considered are intr…
Graph Learning Approaches to Recommender Systems: A Review
Shoujin Wang, Liang Hu, Yan Wang +7
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ the advanced graph learning approaches…
Sequential Recommender Systems: Challenges, Progress and Prospects
Shoujin Wang, Liang Hu, Yan Wang +3
The emerging topic of sequential recommender systems has attracted increasing attention in recent years.Different from the conventional recommender systems including collaborative…
A Survey on Session-based Recommender Systems
Shoujin Wang, Longbing Cao, Yan Wang +3
Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized e…