5 papers
Behavior-Aware Dual-Channel Preference Learning for Heterogeneous Sequential Recommendation
Jing Xiao, Dongqi Wu, Liwei Pan +3
Heterogeneous sequential recommendation (HSR) aims to learn dynamic behavior dependencies from the diverse behaviors of user-item interactions to facilitate precise sequential reco…
Towards Multi-Behavior Multi-Task Recommendation via Behavior-informed Graph Embedding Learning
Wenhao Lai, Weike Pan, Zhong Ming
Multi-behavior recommendation (MBR) aims to improve the performance w.r.t. the target behavior (i.e., purchase) by leveraging auxiliary behaviors (e.g., click, favourite). However,…
Self-Supervised Representation Learning with ID-Content Modality Alignment for Sequential Recommendation
Donglin Zhou, Weike Pan, Zhong Ming
Sequential recommendation (SR) models often capture user preferences based on the historically interacted item IDs, which usually obtain sub-optimal performance when the interactio…
A Survey on Sequential Recommendation
Liwei Pan, Weike Pan, Meiyan Wei +2
Different from most conventional recommendation problems, sequential recommendation focuses on learning users' preferences by exploiting the internal order and dependency among the…
Lossless and Privacy-Preserving Graph Convolution Network for Federated Item Recommendation
Guowei Wu, Weike Pan, Qiang Yang +1
Graph neural network (GNN) has emerged as a state-of-the-art solution for item recommendation. However, existing GNN-based recommendation methods rely on a centralized storage of f…