12 citations · 39 across the 6 of their papers we have counts for
7 papers · 1 filter
CausCF: Causal Collaborative Filtering for RecommendationEffect Estimation
Xu Xie, Zhaoyang Liu, Shiwen Wu +6
To improve user experience and profits of corporations, modern industrial recommender systems usually aim to select the items that are most likely to be interacted with (e.g., clic…
GRN: Generative Rerank Network for Context-wise Recommendation
Yufei Feng, Binbin Hu, Yu Gong +3
Reranking is attracting incremental attention in the recommender systems, which rearranges the input ranking list into the final rank-ing list to better meet user demands. Most exi…
Revisit Recommender System in the Permutation Prospective
Yufei Feng, Yu Gong, Fei Sun +2
Recommender systems (RS) work effective at alleviating information overload and matching user interests in various web-scale applications. Most RS retrieve the user's favorite cand…
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…
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
Yufei Feng, Fuyu Lv, Binbin Hu +5
Click-through rate (CTR) prediction is a critical task for many industrial systems, such as display advertising and recommender systems. Recently, modeling user behavior sequences…