132 citations · 328 across the 10 of their papers we have counts for
12 papers · 1 filter
Inverse Learning with Extremely Sparse Feedback for Recommendation
Guanyu Lin, Chen Gao, Yu Zheng +8
Modern personalized recommendation services often rely on user feedback, either explicit or implicit, to improve the quality of services. Explicit feedback refers to behaviors like…
Disentangling Long and Short-Term Interests for Recommendation
Yu Zheng, Chen Gao, Jianxin Chang +4
Modeling user's long-term and short-term interests is crucial for accurate recommendation. However, since there is no manually annotated label for user interests, existing approach…
Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks
Zirui Zhu, Chen Gao, Xu Chen +3
Incorporating social relations into the recommendation system, i.e. social recommendation, has been widely studied in academic and industrial communities. While many promising resu…
Improving Location Recommendation with Urban Knowledge Graph
Chang Liu, Chen Gao, Depeng Jin +1
Location recommendation is defined as to recommend locations (POIs) to users in location-based services. The existing data-driving approaches of location recommendation suffer from…
DGCN: Diversified Recommendation with Graph Convolutional Networks
Yu Zheng, Chen Gao, Liang Chen +2
These years much effort has been devoted to improving the accuracy or relevance of the recommendation system. Diversity, a crucial factor which measures the dissimilarity among the…
Efficient Data-specific Model Search for Collaborative Filtering
Chen Gao, Quanming Yao, Depeng Jin +1
Collaborative filtering (CF), as a fundamental approach for recommender systems, is usually built on the latent factor model with learnable parameters to predict users' preferences…