169 citations · 229 across the 10 of their papers we have counts for
11 papers
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…
DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching
Kun Zhang, Guangyi Lv, Meng Wang +1
Sentence semantic matching requires an agent to determine the semantic relation between two sentences, where much recent progress has been made by the advancement of representation…
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~(…
Learning Fair Representations for Recommendation: A Graph-based Perspective
Le Wu, Lei Chen, Pengyang Shao +3
As a key application of artificial intelligence, recommender systems are among the most pervasive computer aided systems to help users find potential items of interests. Recently,…
R-Net: Relation of Relation Learning Network for Sentence Semantic Matching
Kun Zhang, Le Wu, Guangyi Lv +3
Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recentl…