Hierarchical User Intention–Preference for Sequential Recommendation with Relation-Aware Heterogeneous Information Network Embedding-Reference-Cited by-同舟云学术

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Hierarchical User Intention–Preference for Sequential Recommendation with Relation-Aware Heterogeneous Information Network Embedding

Published:2022-10-01 Issue:5 Volume:10 Page:466-478
ISSN:2167-6461
Container-title:Big Data
language:en
Short-container-title:Big Data

Author:

Yang Fan¹^ORCID,Li Gangmin²,Yue Yong¹

Affiliation:

1. Department of Computer Science, School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, China.

2. Department of Computer Science, School of Computer Science and Technology, University of Bedfordshire, Luton, United Kingdom.

Publisher

Mary Ann Liebert Inc

Subject

Information Systems and Management,Computer Science Applications,Information Systems

Link

https://www.liebertpub.com/doi/pdf/10.1089/big.2021.0395

Reference52 articles.

1. 1. Hu YF, Koren Y, Volinsky C. Collaborative filtering for implicit feedback datasets. In: 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy: IEEE, December 15–19, 2008. pp. 263–272.

2. 2. Linden G, Smith B, York J. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Intern Comput. 2003;7:76–80.

3. Matrix Factorization Techniques for Recommender Systems

4. 4. Li Z, Zhao HK, Liu Q, et al. Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors. In: Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining, London, UK: ACM, July 19, 2018. pp. 1734–1743.

5. Shifting matrix management: a model for multi-agent cooperation

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