13 citations · 27 across the 3 of their papers we have counts for
7 papers
Graph Attention Collaborative Similarity Embedding for Recommender System
Jinbo Song, Chao Chang, Fei Sun +3
We present Graph Attention Collaborative Similarity Embedding (GACSE), a new recommendation framework that exploits collaborative information in the user-item bipartite graph for r…
NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation
Jinbo Song, Chao Chang, Fei Sun +2
Learning informative representations (aka. embeddings) of users and items is the core of modern recommender systems. Previous works exploit user-item relationships of one-hop neigh…
Compositional Network Embedding
Tianshu Lyu, Fei Sun, Peng Jiang +2
Network embedding has proved extremely useful in a variety of network analysis tasks such as node classification, link prediction, and network visualization. Almost all the existin…
Personalized Re-ranking for Recommendation
Changhua Pei, Yi Zhang, Yongfeng Zhang +6
Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users. Typically, a ranking function is learned from the labeled dataset to optim…
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Fei Sun, Jun Liu, Jian Wu +4
Modeling users' dynamic and evolving preferences from their historical behaviors is challenging and crucial for recommendation systems. Previous methods employ sequential neural ne…
Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems
Changhua Pei, Xinru Yang, Qing Cui +5
Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-…