Recommendation Method Integrating Review Text Hierarchical Attention with Time Information

The data sparsity problem caused by information overload restricts the recommendation performance of the matrix factorization model based on scoring data. The recommendation model integrated with reviews text can effectively alleviate the sparsity of scoring data. The current recommendation system u...

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Autor principal:	XING Changzheng, GUO Yalan, ZHANG Quangui, ZHAO Hongbao
Formato:	article
Lenguaje:	ZH
Publicado:	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2021
Materias:	recommendation system review text hierarchical attention time information Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/2246bf6ce20348d9ab3d522c64a36f7c
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id	oai:doaj.org-article:2246bf6ce20348d9ab3d522c64a36f7c
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spelling	oai:doaj.org-article:2246bf6ce20348d9ab3d522c64a36f7c2021-11-10T08:32:00ZRecommendation Method Integrating Review Text Hierarchical Attention with Time Information10.3778/j.issn.1673-9418.20090351673-9418https://doaj.org/article/2246bf6ce20348d9ab3d522c64a36f7c2021-11-01T00:00:00Zhttp://fcst.ceaj.org/CN/abstract/abstract2960.shtmlhttps://doaj.org/toc/1673-9418The data sparsity problem caused by information overload restricts the recommendation performance of the matrix factorization model based on scoring data. The recommendation model integrated with reviews text can effectively alleviate the sparsity of scoring data. The current recommendation system uses reviews text to model users and items, most of them use users' reviews text on items as the data sources, and ignore the impacts of time information on user and item attributes. In response to this problem, a recommendation method which integrates review text hierarchical attention with time information (RHATR) is proposed. This method can fully mine the potential semantic information of review text and model the dynamic changes of user perferences and item features. This method mines effective information such as sentiment words and keywords in a single review text, learns user and item representation by applying word level attention to a single review text, applies review level attention to extracting effective reviews respectively for the user review set and item review set with temporal factor and further learns the dynamic representation of user preferences and item features. Finally, the user and item representation learned from the review text, and ID based item and user embedding as the final features, it captures the potential factors of each user and item. The experimental results show that the proposed method has a better effect in root mean square error (RMSE) on Amazon and Yelp datasets than the current baseline method.XING Changzheng, GUO Yalan, ZHANG Quangui, ZHAO HongbaoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science Pressarticlerecommendation systemreview texthierarchical attentiontime informationElectronic computers. Computer scienceQA75.5-76.95ZHJisuanji kexue yu tansuo, Vol 15, Iss 11, Pp 2222-2232 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	recommendation system review text hierarchical attention time information Electronic computers. Computer science QA75.5-76.95
spellingShingle	recommendation system review text hierarchical attention time information Electronic computers. Computer science QA75.5-76.95 XING Changzheng, GUO Yalan, ZHANG Quangui, ZHAO Hongbao Recommendation Method Integrating Review Text Hierarchical Attention with Time Information
description	The data sparsity problem caused by information overload restricts the recommendation performance of the matrix factorization model based on scoring data. The recommendation model integrated with reviews text can effectively alleviate the sparsity of scoring data. The current recommendation system uses reviews text to model users and items, most of them use users' reviews text on items as the data sources, and ignore the impacts of time information on user and item attributes. In response to this problem, a recommendation method which integrates review text hierarchical attention with time information (RHATR) is proposed. This method can fully mine the potential semantic information of review text and model the dynamic changes of user perferences and item features. This method mines effective information such as sentiment words and keywords in a single review text, learns user and item representation by applying word level attention to a single review text, applies review level attention to extracting effective reviews respectively for the user review set and item review set with temporal factor and further learns the dynamic representation of user preferences and item features. Finally, the user and item representation learned from the review text, and ID based item and user embedding as the final features, it captures the potential factors of each user and item. The experimental results show that the proposed method has a better effect in root mean square error (RMSE) on Amazon and Yelp datasets than the current baseline method.
format	article
author	XING Changzheng, GUO Yalan, ZHANG Quangui, ZHAO Hongbao
author_facet	XING Changzheng, GUO Yalan, ZHANG Quangui, ZHAO Hongbao
author_sort	XING Changzheng, GUO Yalan, ZHANG Quangui, ZHAO Hongbao
title	Recommendation Method Integrating Review Text Hierarchical Attention with Time Information
title_short	Recommendation Method Integrating Review Text Hierarchical Attention with Time Information
title_full	Recommendation Method Integrating Review Text Hierarchical Attention with Time Information
title_fullStr	Recommendation Method Integrating Review Text Hierarchical Attention with Time Information
title_full_unstemmed	Recommendation Method Integrating Review Text Hierarchical Attention with Time Information
title_sort	recommendation method integrating review text hierarchical attention with time information
publisher	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
publishDate	2021
url	https://doaj.org/article/2246bf6ce20348d9ab3d522c64a36f7c
work_keys_str_mv	AT xingchangzhengguoyalanzhangquanguizhaohongbao recommendationmethodintegratingreviewtexthierarchicalattentionwithtimeinformation
_version_	1718440356597465088

Recommendation Method Integrating Review Text Hierarchical Attention with Time Information

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