MixGCF
Author:
Affiliation:
1. Zhejiang University, Hangzhou, China
2. Facebook AI, Seattle, WA, USA
3. Tsinghua University, Beijing, China
Funder
National Science Foundation for Distinguished Young Scholars
National Natural ScienceFoundation of China Key Program
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3447548.3467408
Reference53 articles.
1. Yixin Cao Xiang Wang Xiangnan He Zikun Hu and Tat-Seng Chua. 2019. Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences. In WWW. Yixin Cao Xiang Wang Xiangnan He Zikun Hu and Tat-Seng Chua. 2019. Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences. In WWW.
2. Lei Chen Le Wu Richang Hong Kun Zhang and Meng Wang. 2020. Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. In AAAI. Lei Chen Le Wu Richang Hong Kun Zhang and Meng Wang. 2020. Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. In AAAI.
3. Ting Chen Yizhou Sun Yue Shi and Liangjie Hong. 2017. On Sampling Strategies for Neural Network-based Collaborative Filtering. In KDD. 767--776. Ting Chen Yizhou Sun Yue Shi and Liangjie Hong. 2017. On Sampling Strategies for Neural Network-based Collaborative Filtering. In KDD. 767--776.
4. Paul Covington Jay Adams and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In RecSys. 191--198. Paul Covington Jay Adams and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In RecSys. 191--198.
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