169 citations · 377 across the 16 of their papers we have counts for
14 papers · 1 filter
MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation
Kang Liu, Feng Xue, Dan Guo +3
In most E-commerce platforms, whether the displayed items trigger the user's interest largely depends on their most eye-catching multimodal content. Consequently, increasing effort…
A Review-aware Graph Contrastive Learning Framework for Recommendation
Jie Shuai, Kun Zhang, Le Wu +4
Most modern recommender systems predict users preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the au…
Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering
Minghao Zhao, Le Wu, Yile Liang +7
Recent years have witnessed the great accuracy performance of graph-based Collaborative Filtering (CF) models for recommender systems. By taking the user-item interaction behavior…
ProFairRec: Provider Fairness-aware News Recommendation
Tao Qi, Fangzhao Wu, Chuhan Wu +5
News recommendation aims to help online news platform users find their preferred news articles. Existing news recommendation methods usually learn models from historical user behav…
Privileged Graph Distillation for Cold Start Recommendation
Shuai Wang, Kun Zhang, Le Wu +3
The cold start problem in recommender systems is a long-standing challenge, which requires recommending to new users (items) based on attributes without any historical interaction…
Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation
Lei Chen, Le Wu, Kun Zhang +2
As users often express their preferences with binary behavior data~(implicit feedback), such as clicking items or buying products, implicit feedback based Collaborative Filtering~(…