Bayesian probabilistic tensor factorization for recommendation and rating aggregation with multicriteria evaluation data

Morise, Hiroki; Oyama, Satoshi; Kurihara, Masahito

doi:10.1016/j.eswa.2019.04.044


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Bayesian probabilistic tensor factorization for recommendation and rating aggregation with multicriteria evaluation data

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/82844

Title:	Bayesian probabilistic tensor factorization for recommendation and rating aggregation with multicriteria evaluation data
Authors:	Morise, Hiroki Browse this author
	Oyama, Satoshi Browse this author →KAKEN DB
	Kurihara, Masahito Browse this author →KAKEN DB
Keywords:	Recommendation
	Multi-criteria rating
	Collaborative filtering
	Rating aggregation
	Bayesian probabilistic models
Issue Date:	1-Oct-2019
Publisher:	Elsevier
Journal Title:	Expert Systems with Applications
Volume:	131
Start Page:	1
End Page:	8
Publisher DOI:	10.1016/j.eswa.2019.04.044
Abstract:	Ratings by users on various items such as products and services have become easily available on the Web. Also available in many cases, in addition to an overall rating for each item by each user, are multicriteria ratings from different viewpoints. Our previous study showed that multicriteria rating approaches performed better than single-criterion ones for both recommendation and rating aggregation. We have now formulated a Bayesian probabilistic model for multicriteria evaluation as an alternative to low-rank approximation. We evaluated the performance of this model, in which model capacity is controlled by integrating over all model parameters, and investigated whether it can be made to work more efficiently by using a Markov chain Monte Carlo method for both recommendation and rating aggregation. It performed better than low-rank approximation methods that obtain a maximum a posteriori estimate by fitting to the data. (C) 2019 Elsevier Ltd. All rights reserved.
Rights:	© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Rights:	https://creativecommons.org/licenses/by-nc-nd/4.0/
Type:	article (author version)
URI:	http://hdl.handle.net/2115/82844
Appears in Collections:	国際連携研究教育局 : GI-CoRE (Global Institution for Collaborative Research and Education : GI-CoRE) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc) 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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