486 citations · 2.3k across the 55 of their papers we have counts for
15 papers · 1 filter
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…
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…
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…
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,…
Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach
Le Wu, Yonghui Yang, Kun Zhang +3
In many recommender systems, users and items are associated with attributes, and users show preferences to items. The attribute information describes users'(items') characteristics…
Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation
Le Wu, Yonghui Yang, Lei Chen +3
With the increasing availability of videos, how to edit them and present the most interesting parts to users, i.e., video highlight, has become an urgent need with many broad appli…
Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
Wenqiang Lei, Xiangnan He, Yisong Miao +4
Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models. A successful Conv…
Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
Lei Chen, Le Wu, Richang Hong +2
Graph Convolutional Networks (GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations an…
Personalized Multimedia Item and Key Frame Recommendation
Le Wu, Lei Chen, Yonghui Yang +4
When recommending or advertising items to users, an emerging trend is to present each multimedia item with a key frame image (e.g., the poster of a movie). As each multimedia item…
A Neural Influence Diffusion Model for Social Recommendation
Le Wu, Peijie Sun, Yanjie Fu +3
Precise user and item embedding learning is the key to building a successful recommender system. Traditionally, Collaborative Filtering(CF) provides a way to learn user and item em…
Deep Item-based Collaborative Filtering for Top-N Recommendation
Feng Xue, Xiangnan He, Xiang Wang +3
Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online per…
Fast Matrix Factorization with Non-Uniform Weights on Missing Data
Xiangnan He, Jinhui Tang, Xiaoyu Du +3
Matrix factorization (MF) has been widely used to discover the low-rank structure and to predict the missing entries of data matrix. In many real-world learning systems, the data m…
SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation
Le Wu, Peijie Sun, Richang Hong +3
Collaborative Filtering (CF) is one of the most successful approaches for recommender systems. With the emergence of online social networks, social recommendation has become a popu…