361 citations · 484 across the 6 of their papers we have counts for
7 papers
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…
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…