52 citations · 62 across the 6 of their papers we have counts for
Showing 2023Show all
3 papers · 1 filter
cs.IR2023★ 3 cited
Generative Contrastive Graph Learning for Recommendation
Yonghui Yang, Zhengwei Wu, Le Wu +5
By treating users' interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering(CF) based recommendation. Recently, researchers hav…
cs.IR2023
Explainable Recommender with Geometric Information Bottleneck
Hanqi Yan, Lin Gui, Menghan Wang +2
Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems either rely on human-annotated…
cs.IR2023★ 6 cited
Improving Recommendation Fairness via Data Augmentation
Lei Chen, Le Wu, Kun Zhang +5
Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. R Recently, the…