232 citations · 483 across the 3 of their papers we have counts for
3 papers
cs.IR2023★ 232 cited
Heterogeneous Graph Contrastive Learning for Recommendation
Mengru Chen, Chao Huang, Lianghao Xia +3
Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured data in recommender systems. However, real-life recommendation scenarios usually involve hetero…
cs.IR2022★ 43 cited
Multi-level Contrastive Learning Framework for Sequential Recommendation
Ziyang Wang, Huoyu Liu, Wei Wei +5
Sequential recommendation (SR) aims to predict the subsequent behaviors of users by understanding their successive historical behaviors. Recently, some methods for SR are devoted t…
cs.IR2022★ 208 cited
Contrastive Meta Learning with Behavior Multiplicity for Recommendation
Wei Wei, Chao Huang, Lianghao Xia +3
A well-informed recommendation framework could not only help users identify their interested items, but also benefit the revenue of various online platforms (e.g., e-commerce, soci…