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Attentional factorization machine with review-based user–item interaction for recommendation

In recommender systems, user reviews on items contain rich semantic information, which can express users’ preferences and item features. However, existing review-based recommendation methods either use the static word vector model or cannot effectively extract long sequence features in reviews, resu...

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Detalles Bibliográficos
Autores principales: Li, Zheng, Jin, Di, Yuan, Ke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439228/
https://www.ncbi.nlm.nih.gov/pubmed/37596385
http://dx.doi.org/10.1038/s41598-023-40633-4
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author Li, Zheng
Jin, Di
Yuan, Ke
author_facet Li, Zheng
Jin, Di
Yuan, Ke
author_sort Li, Zheng
collection PubMed
description In recommender systems, user reviews on items contain rich semantic information, which can express users’ preferences and item features. However, existing review-based recommendation methods either use the static word vector model or cannot effectively extract long sequence features in reviews, resulting in the limited ability of user feature expression. Furthermore, the impact of different or useless feature interactions between users and items on recommendation performance is ignored. Therefore, we propose an attentional factorization machine with review-based user–item interaction for recommendation (AFMRUI), which first leverages RoBERTa to obtain the embedding feature of each user/item review, and combines bidirectional gated recurrent units with attention network to highlight more useful information in both user and item reviews. Then we adopt AFM to learn user–item feature interactions to distinguish the importance of different user–item feature interactions and further to obtain more accurate rating prediction, so as to promote recommendation. Finally, we conducted performance evaluation on five real-world datasets. Experimental results on five datasets demonstrated that the proposed AFMRUI outperformed the state-of-the-art review-based methods regarding two commonly used evaluation metrics.
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spelling pubmed-104392282023-08-20 Attentional factorization machine with review-based user–item interaction for recommendation Li, Zheng Jin, Di Yuan, Ke Sci Rep Article In recommender systems, user reviews on items contain rich semantic information, which can express users’ preferences and item features. However, existing review-based recommendation methods either use the static word vector model or cannot effectively extract long sequence features in reviews, resulting in the limited ability of user feature expression. Furthermore, the impact of different or useless feature interactions between users and items on recommendation performance is ignored. Therefore, we propose an attentional factorization machine with review-based user–item interaction for recommendation (AFMRUI), which first leverages RoBERTa to obtain the embedding feature of each user/item review, and combines bidirectional gated recurrent units with attention network to highlight more useful information in both user and item reviews. Then we adopt AFM to learn user–item feature interactions to distinguish the importance of different user–item feature interactions and further to obtain more accurate rating prediction, so as to promote recommendation. Finally, we conducted performance evaluation on five real-world datasets. Experimental results on five datasets demonstrated that the proposed AFMRUI outperformed the state-of-the-art review-based methods regarding two commonly used evaluation metrics. Nature Publishing Group UK 2023-08-18 /pmc/articles/PMC10439228/ /pubmed/37596385 http://dx.doi.org/10.1038/s41598-023-40633-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Li, Zheng
Jin, Di
Yuan, Ke
Attentional factorization machine with review-based user–item interaction for recommendation
title Attentional factorization machine with review-based user–item interaction for recommendation
title_full Attentional factorization machine with review-based user–item interaction for recommendation
title_fullStr Attentional factorization machine with review-based user–item interaction for recommendation
title_full_unstemmed Attentional factorization machine with review-based user–item interaction for recommendation
title_short Attentional factorization machine with review-based user–item interaction for recommendation
title_sort attentional factorization machine with review-based user–item interaction for recommendation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439228/
https://www.ncbi.nlm.nih.gov/pubmed/37596385
http://dx.doi.org/10.1038/s41598-023-40633-4
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