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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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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. |
format | Online Article Text |
id | pubmed-7660452 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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