KEMIM: Knowledge-Enhanced User Multi-Interest Modeling for Recommender Systems
Author:
Affiliation:
1. School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China
2. School of Computer Science and Technology, University of Bedfordshire, Luton, U.K
3. Department of Computer Science, University of Liverpool, Liverpool, U.K
Funder
Basic Public Welfare Research Project of Zhejiang, China
AI University Research Centre (AI-URC) through the Xi’an Jiaotong-Liverpool University Key Program Special Fund
Suzhou Municipal Key Laboratory for Intelligent Virtual Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10092818.pdf?arnumber=10092818
Reference39 articles.
1. KGAT
2. A Hierarchical Knowledge and Interest Propagation Network for Recommender Systems
3. Inductive representation learning on large graphs;hamilton;Proc Adv Neural Inf Process Syst,2017
4. SoRS: Social recommendation using global rating reputation and local rating similarity
5. Personalized entity recommendation
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