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To develop a knowledge-aware recommender system, a key data problem is how we can obtain rich and structured knowledge information for recommender system (RS) items.
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BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI
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Personalized entity recommendation: a heterogeneous information network approach. In WSDM . 283–292
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Collaborative Knowledge Base Embedding for Recommender Systems. In SIGKDD . 353–362
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Knowledge Graph Embedding: A Survey of Approaches and Applications
Quan Wang, Zhendong Mao, Bin Wang, and Li Guo. 2017 · 2017
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Translation-based Recommendation: A Scalable Method for Modeling Sequential Behavior. In IJCAI
Ruining He, Wang-Cheng Kang, and Julian McAuley. 2018 · 2018
Closest in time.
Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model. In SIGKDD . 1531–1540
Binbin Hu, Chuan Shi, Wayne Xin Zhao, and Philip S. Yu. 2018 · 2018
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Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In WWW
Ruining He and Julian Mcauley. 2016 · 2016
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SPrank: Semantic Path-Based Ranking for Top- N Recommendations Using Linked Open Data
Tommaso Di Noia, Vito Claudio Ostuni, Paolo Tomeo, and Eugenio Di Sciascio. 2016 · 2016
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DKN: Deep Knowledge-Aware Network for News Recommendation. In WWW . 1835–1844
Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018b
Cited in the paper.
Jin Huang, Wayne Xin Zhao, Hong-Jian Dou, Ji-Rong Wen, and Edward Y. Chang. 2018 · 2018
Closest in time.
Ripple Network: Propagating User Preferences on the Knowledge Graph for Recommender Systems
Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2018a · 2018
Closest in time.