1.3k citations
- Tsinghua UniversityCN130 papers
- Chinese University of Hong KongHK81 papers
- Chinese Academy of SciencesCN71 papers
- Peking UniversityCN68 papers
- Shanghai Jiao Tong UniversityCN58 papers
- Zhejiang UniversityCN54 papers
- University of Chinese Academy of SciencesCN52 papers
- Hong Kong University of Science and TechnologyHK46 papers
- Xiamen UniversityCN37 papers
- University of Hong KongHK34 papers
- Peng Cheng LaboratoryCN31 papers
- City University of Hong KongHK30 papers
10 papers · 2 filters
DiVa: An Iterative Framework to Harvest More Diverse and Valid Labels from User Comments for Music
Hongru Liang, Jingyao Liu, Yuanxin Xiang +4
Towards sufficient music searching, it is vital to form a complete set of labels for each song. However, current solutions fail to resolve it as they cannot produce diverse enough…
Temporal Interest Network for User Response Prediction
Haolin Zhou, Junwei Pan, Xinyi Zhou +4
User response prediction is essential in industrial recommendation systems, such as online display advertising. Among all the features in recommendation models, user behaviors are…
Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
Yan Wen, Chen Gao, Lingling Yi +3
Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either…
Knowledge Graph Self-Supervised Rationalization for Recommendation
Yuhao Yang, Chao Huang, Lianghao Xia +1
In this paper, we introduce a new self-supervised rationalization method, called KGRec, for knowledge-aware recommender systems. To effectively identify informative knowledge conne…
Unbiased Pairwise Learning from Implicit Feedback for Recommender Systems without Biased Variance Control
Yi Ren, Hongyan Tang, Jiangpeng Rong +1
Generally speaking, the model training for recommender systems can be based on two types of data, namely explicit feedback and implicit feedback. Moreover, because of its general a…
Debiased Contrastive Learning for Sequential Recommendation
Yuhao Yang, Chao Huang, Lianghao Xia +3
Current sequential recommender systems are proposed to tackle the dynamic user preference learning with various neural techniques, such as Transformer and Graph Neural Networks (GN…