Joint Multi-Grained Popularity-Aware Graph Convolution Collaborative Filtering for Recommendation-Reference-Cited by-同舟云学术

Joint Multi-Grained Popularity-Aware Graph Convolution Collaborative Filtering for Recommendation

Published:2023-02 Issue:1 Volume:10 Page:72-83
ISSN:2329-924X
Container-title:IEEE Transactions on Computational Social Systems
language:
Short-container-title:IEEE Trans. Comput. Soc. Syst.

Author:

Liu Kang¹^ORCID,Xue Feng²^ORCID,He Xiangnan³^ORCID,Guo Dan¹^ORCID,Hong Richang¹

Affiliation:

1. Key Laboratory of Knowledge Engineering With Big Data, Intelligent Interconnected Systems Laboratory of Anhui Province, and School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China

2. Key Laboratory of Knowledge Engineering With Big Data, Intelligent Interconnected Systems Laboratory of Anhui Province, and School of Software, Hefei University of Technology, Hefei, China

3. School of Data Science, University of Science and Technology of China, Hefei, China

Funder

Special Support Plan for Innovation and Entrepreneurship in Anhui Province

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Human-Computer Interaction,Social Sciences (miscellaneous),Modeling and Simulation

Link

http://xplorestaging.ieee.org/ielx7/6570650/10032687/09730040.pdf?arnumber=9730040

Reference42 articles.

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2. Collaborative Knowledge Base Embedding for Recommender Systems

3. Semi-supervised classification with graph convolutional networks;kipf;Proc 5th Int Conf Learn Represent (ICLR),2017

4. Deep Neural Networks for YouTube Recommendations

5. Graph convolutional matrix completion;van den berg;arXiv 1706 02263,2017

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