Measuring Product Similarity with Hesitant Fuzzy Set for Recommendation

The processing of a sparse matrix is a hot topic in the recommendation system. This paper applies the method of hesitant fuzzy set to study the sparse matrix processing problem. Based on the uncertain factors in the recommendation process, this paper applies hesitant fuzzy set theory to characterize...

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Autores principales:	Chunsheng Cui, Jielu Li, Zhenchun Zang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	hesitant fuzzy set recommendation system sparse matrix similarity Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/09b711f16e5e4a54ba8fbcb330292a4f
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id	oai:doaj.org-article:09b711f16e5e4a54ba8fbcb330292a4f
record_format	dspace
spelling	oai:doaj.org-article:09b711f16e5e4a54ba8fbcb330292a4f2021-11-11T18:13:59ZMeasuring Product Similarity with Hesitant Fuzzy Set for Recommendation10.3390/math92126572227-7390https://doaj.org/article/09b711f16e5e4a54ba8fbcb330292a4f2021-10-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2657https://doaj.org/toc/2227-7390The processing of a sparse matrix is a hot topic in the recommendation system. This paper applies the method of hesitant fuzzy set to study the sparse matrix processing problem. Based on the uncertain factors in the recommendation process, this paper applies hesitant fuzzy set theory to characterize the historical ratings embedded in the recommendation system and studies the data processing problem of the sparse matrix under the condition of a hesitant fuzzy set. The key is to transform the similarity problem of products in the sparse matrix into the similarity problem of two hesitant fuzzy sets by data conversion, data processing, and data complement. This paper further considers the influence of the difference of user ratings on the recommendation results and obtains a user’s recommendation list. On the one hand, the proposed method effectively solves the matrix in the recommendation system; on the other hand, it provides a feasible method for calculating similarity in the recommendation system.Chunsheng CuiJielu LiZhenchun ZangMDPI AGarticlehesitant fuzzy setrecommendation systemsparse matrixsimilarityMathematicsQA1-939ENMathematics, Vol 9, Iss 2657, p 2657 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hesitant fuzzy set recommendation system sparse matrix similarity Mathematics QA1-939
spellingShingle	hesitant fuzzy set recommendation system sparse matrix similarity Mathematics QA1-939 Chunsheng Cui Jielu Li Zhenchun Zang Measuring Product Similarity with Hesitant Fuzzy Set for Recommendation
description	The processing of a sparse matrix is a hot topic in the recommendation system. This paper applies the method of hesitant fuzzy set to study the sparse matrix processing problem. Based on the uncertain factors in the recommendation process, this paper applies hesitant fuzzy set theory to characterize the historical ratings embedded in the recommendation system and studies the data processing problem of the sparse matrix under the condition of a hesitant fuzzy set. The key is to transform the similarity problem of products in the sparse matrix into the similarity problem of two hesitant fuzzy sets by data conversion, data processing, and data complement. This paper further considers the influence of the difference of user ratings on the recommendation results and obtains a user’s recommendation list. On the one hand, the proposed method effectively solves the matrix in the recommendation system; on the other hand, it provides a feasible method for calculating similarity in the recommendation system.
format	article
author	Chunsheng Cui Jielu Li Zhenchun Zang
author_facet	Chunsheng Cui Jielu Li Zhenchun Zang
author_sort	Chunsheng Cui
title	Measuring Product Similarity with Hesitant Fuzzy Set for Recommendation
title_short	Measuring Product Similarity with Hesitant Fuzzy Set for Recommendation
title_full	Measuring Product Similarity with Hesitant Fuzzy Set for Recommendation
title_fullStr	Measuring Product Similarity with Hesitant Fuzzy Set for Recommendation
title_full_unstemmed	Measuring Product Similarity with Hesitant Fuzzy Set for Recommendation
title_sort	measuring product similarity with hesitant fuzzy set for recommendation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/09b711f16e5e4a54ba8fbcb330292a4f
work_keys_str_mv	AT chunshengcui measuringproductsimilaritywithhesitantfuzzysetforrecommendation AT jieluli measuringproductsimilaritywithhesitantfuzzysetforrecommendation AT zhenchunzang measuringproductsimilaritywithhesitantfuzzysetforrecommendation
_version_	1718431863965483008

Measuring Product Similarity with Hesitant Fuzzy Set for Recommendation

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