Bayesian personalized ranking based on multiple-layer neighborhoods

Hu, Y; Xiong, F; Pan, S; Xiong, X; Wang, L; Chen, H

Bayesian personalized ranking based on multiple-layer neighborhoods

Hu, Y Xiong, F Pan, S

Xiong, X Wang, L Chen, H

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Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: Information Sciences, 2021, 542, pp. 156-176
Issue Date:: 2021-01-04

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Field	Value	Language
dc.contributor.author	Hu, Y
dc.contributor.author	Xiong, F
dc.contributor.author	Pan, S https://orcid.org/0000-0003-0794-527X
dc.contributor.author	Xiong, X
dc.contributor.author	Wang, L
dc.contributor.author	Chen, H
dc.date.accessioned	2022-03-19T23:23:33Z
dc.date.available	2022-03-19T23:23:33Z
dc.date.issued	2021-01-04
dc.identifier.citation	Information Sciences, 2021, 542, pp. 156-176
dc.identifier.issn	0020-0255
dc.identifier.issn	1872-6291
dc.identifier.uri	http://hdl.handle.net/10453/155379
dc.description.abstract	Recommender systems are widely used on the Internet as tools for data analysis, processing and discovery. Traditional recommendation algorithms mostly exploit rating information in a simple way while ignoring some hidden information in ratings, thus restricting recommendation performance. This hidden information in ratings, such as similarities between rated items and items unrated by the same user, can unveil the relationships between users and items by using multiple layers to help find the preferences of users. To focus on this hidden information, we propose a new Bayesian Personalized Ranking algorithm based on multiple-layer neighborhoods (BPRN). We divide items into different sets based on the analysis of user-item relevance and give an order for the sets. Then, we use BPRN to obtain the fine-grained order of items in different sets and finally generate a personalized, sorted list for each user. We have used five real-world datasets to test the accuracy of BPRN and compare its performance with state-of-the-art models. Experiments show that our algorithm greatly improves the accuracy of the recommendation results. In addition, our algorithm distinctly alleviates the problems of data sparsity and cold-start users.
dc.language	English
dc.publisher	ELSEVIER SCIENCE INC
dc.relation.ispartof	Information Sciences
dc.relation.isbasedon	10.1016/j.ins.2020.06.067
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Bayesian personalized ranking based on multiple-layer neighborhoods
dc.type	Journal Article
utslib.citation.volume	542
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-03-19T23:23:31Z
pubs.publication-status	Published
pubs.volume	542

Abstract:

Recommender systems are widely used on the Internet as tools for data analysis, processing and discovery. Traditional recommendation algorithms mostly exploit rating information in a simple way while ignoring some hidden information in ratings, thus restricting recommendation performance. This hidden information in ratings, such as similarities between rated items and items unrated by the same user, can unveil the relationships between users and items by using multiple layers to help find the preferences of users. To focus on this hidden information, we propose a new Bayesian Personalized Ranking algorithm based on multiple-layer neighborhoods (BPRN). We divide items into different sets based on the analysis of user-item relevance and give an order for the sets. Then, we use BPRN to obtain the fine-grained order of items in different sets and finally generate a personalized, sorted list for each user. We have used five real-world datasets to test the accuracy of BPRN and compare its performance with state-of-the-art models. Experiments show that our algorithm greatly improves the accuracy of the recommendation results. In addition, our algorithm distinctly alleviates the problems of data sparsity and cold-start users.

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http://hdl.handle.net/10453/155379