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Global and Local Tensor Factorization for Multi-criteria Recommender System

In multi-criteria recommender systems, matrix factorization characterizes users and items via latent factor vectors inferred from user-item rating patterns. However, two-dimensional matrix factorization models may not be able to cope with the recommendation problem that involves additional criterion...

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Detalles Bibliográficos
Autores principales: Wang, Shuliang, Yang, Jingting, Chen, Zhengyu, Yuan, Hanning, Geng, Jing, Hai, Zhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660452/
https://www.ncbi.nlm.nih.gov/pubmed/33205096
http://dx.doi.org/10.1016/j.patter.2020.100023
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author Wang, Shuliang
Yang, Jingting
Chen, Zhengyu
Yuan, Hanning
Geng, Jing
Hai, Zhen
author_facet Wang, Shuliang
Yang, Jingting
Chen, Zhengyu
Yuan, Hanning
Geng, Jing
Hai, Zhen
author_sort Wang, Shuliang
collection PubMed
description In multi-criteria recommender systems, matrix factorization characterizes users and items via latent factor vectors inferred from user-item rating patterns. However, two-dimensional matrix factorization models may not be able to cope with the recommendation problem that involves additional criterion-specific rating data. This study introduces a tensor factorization method to handle three-dimensional user-item-criterion rating data. Moreover, we observe that using single global tensor factorization alone may not be sufficient to characterize diverse preferences among different groups of users, and a combined global and local tensor factorization method (GLTF) for multi-criteria recommendation is thus proposed. One key benefit of the GLTF is that it can leverage global user-item-criterion rating patterns while also exploiting local user-subset specific rating behaviors to jointly infer the latent factor representations for users, items, and specific item criteria. Experimental results, which used real-life data available to the public, demonstrated that the GLTF is superior to well-established baseline methods.
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spelling pubmed-76604522020-11-16 Global and Local Tensor Factorization for Multi-criteria Recommender System Wang, Shuliang Yang, Jingting Chen, Zhengyu Yuan, Hanning Geng, Jing Hai, Zhen Patterns (N Y) Article In multi-criteria recommender systems, matrix factorization characterizes users and items via latent factor vectors inferred from user-item rating patterns. However, two-dimensional matrix factorization models may not be able to cope with the recommendation problem that involves additional criterion-specific rating data. This study introduces a tensor factorization method to handle three-dimensional user-item-criterion rating data. Moreover, we observe that using single global tensor factorization alone may not be sufficient to characterize diverse preferences among different groups of users, and a combined global and local tensor factorization method (GLTF) for multi-criteria recommendation is thus proposed. One key benefit of the GLTF is that it can leverage global user-item-criterion rating patterns while also exploiting local user-subset specific rating behaviors to jointly infer the latent factor representations for users, items, and specific item criteria. Experimental results, which used real-life data available to the public, demonstrated that the GLTF is superior to well-established baseline methods. Elsevier 2020-05-08 /pmc/articles/PMC7660452/ /pubmed/33205096 http://dx.doi.org/10.1016/j.patter.2020.100023 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Wang, Shuliang
Yang, Jingting
Chen, Zhengyu
Yuan, Hanning
Geng, Jing
Hai, Zhen
Global and Local Tensor Factorization for Multi-criteria Recommender System
title Global and Local Tensor Factorization for Multi-criteria Recommender System
title_full Global and Local Tensor Factorization for Multi-criteria Recommender System
title_fullStr Global and Local Tensor Factorization for Multi-criteria Recommender System
title_full_unstemmed Global and Local Tensor Factorization for Multi-criteria Recommender System
title_short Global and Local Tensor Factorization for Multi-criteria Recommender System
title_sort global and local tensor factorization for multi-criteria recommender system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660452/
https://www.ncbi.nlm.nih.gov/pubmed/33205096
http://dx.doi.org/10.1016/j.patter.2020.100023
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