Implicit Feedback Recommendation Method Based on User-Generated Content

Studying recommendation method has long been a fundamental area in personalized marketing science. The rating data sparsity problem is the biggest challenge of recommendations. In addition, existing recommendation methods can only identify user preferences rather than customer needs. To solve these...

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Autores principales:	Bing Fang, Enpeng Hu, Junyang Shen, Jingwen Zhang, Yang Chen
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/cfbd2c6a4087411d9d777aceaa9f3bde
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spelling	oai:doaj.org-article:cfbd2c6a4087411d9d777aceaa9f3bde2021-11-08T02:36:28ZImplicit Feedback Recommendation Method Based on User-Generated Content1875-919X10.1155/2021/3982270https://doaj.org/article/cfbd2c6a4087411d9d777aceaa9f3bde2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3982270https://doaj.org/toc/1875-919XStudying recommendation method has long been a fundamental area in personalized marketing science. The rating data sparsity problem is the biggest challenge of recommendations. In addition, existing recommendation methods can only identify user preferences rather than customer needs. To solve these two bottleneck problems, we propose a novel implicit feedback recommendation method using user-generated content (UGC). We identify product feature and customer needs from UGC using Convolutional Neural Network (CNN) model and textual semantic analysis techniques, measure user-product fit degree introducing attention mechanism and antonym mechanism, and predict user rating based on user-product fit degree and user history rating data. Using data from a large-scale review sites, we demonstrate the effectiveness of our proposed method. Our study makes several research contributions. First, we propose a novel recommendation method with strong robustness against sparse rating data. Second, we propose a novel recommendation method based on the customer need-product feature fit. Third, we propose a novel approach to measure the fit degree of customer needs-product feature, which can effectively improve the performance of recommendation method. Our study also indicates the following findings: (1) UGC can be used to predict user ratings with no user rating records. This finding has important implications to solve the sparsity problem of recommendations thoroughly. (2) The customer need-based recommendation method has better performance than existing user preference-based recommendation methods. This finding sheds light on the necessity of mining customer need for recommendation methods. (3) UGC can be used to mine customer need and product features. This finding indicates that UGC also can be used in the other studies requiring information about customer need and product feature. (4) Comparing the opinions of user review should not be solely on the basis of semantic similarity. This finding sheds light on the limitation of existing opinion mining studies.Bing FangEnpeng HuJunyang ShenJingwen ZhangYang ChenHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Bing Fang Enpeng Hu Junyang Shen Jingwen Zhang Yang Chen Implicit Feedback Recommendation Method Based on User-Generated Content
description	Studying recommendation method has long been a fundamental area in personalized marketing science. The rating data sparsity problem is the biggest challenge of recommendations. In addition, existing recommendation methods can only identify user preferences rather than customer needs. To solve these two bottleneck problems, we propose a novel implicit feedback recommendation method using user-generated content (UGC). We identify product feature and customer needs from UGC using Convolutional Neural Network (CNN) model and textual semantic analysis techniques, measure user-product fit degree introducing attention mechanism and antonym mechanism, and predict user rating based on user-product fit degree and user history rating data. Using data from a large-scale review sites, we demonstrate the effectiveness of our proposed method. Our study makes several research contributions. First, we propose a novel recommendation method with strong robustness against sparse rating data. Second, we propose a novel recommendation method based on the customer need-product feature fit. Third, we propose a novel approach to measure the fit degree of customer needs-product feature, which can effectively improve the performance of recommendation method. Our study also indicates the following findings: (1) UGC can be used to predict user ratings with no user rating records. This finding has important implications to solve the sparsity problem of recommendations thoroughly. (2) The customer need-based recommendation method has better performance than existing user preference-based recommendation methods. This finding sheds light on the necessity of mining customer need for recommendation methods. (3) UGC can be used to mine customer need and product features. This finding indicates that UGC also can be used in the other studies requiring information about customer need and product feature. (4) Comparing the opinions of user review should not be solely on the basis of semantic similarity. This finding sheds light on the limitation of existing opinion mining studies.
format	article
author	Bing Fang Enpeng Hu Junyang Shen Jingwen Zhang Yang Chen
author_facet	Bing Fang Enpeng Hu Junyang Shen Jingwen Zhang Yang Chen
author_sort	Bing Fang
title	Implicit Feedback Recommendation Method Based on User-Generated Content
title_short	Implicit Feedback Recommendation Method Based on User-Generated Content
title_full	Implicit Feedback Recommendation Method Based on User-Generated Content
title_fullStr	Implicit Feedback Recommendation Method Based on User-Generated Content
title_full_unstemmed	Implicit Feedback Recommendation Method Based on User-Generated Content
title_sort	implicit feedback recommendation method based on user-generated content
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/cfbd2c6a4087411d9d777aceaa9f3bde
work_keys_str_mv	AT bingfang implicitfeedbackrecommendationmethodbasedonusergeneratedcontent AT enpenghu implicitfeedbackrecommendationmethodbasedonusergeneratedcontent AT junyangshen implicitfeedbackrecommendationmethodbasedonusergeneratedcontent AT jingwenzhang implicitfeedbackrecommendationmethodbasedonusergeneratedcontent AT yangchen implicitfeedbackrecommendationmethodbasedonusergeneratedcontent
_version_	1718443139914530816

Implicit Feedback Recommendation Method Based on User-Generated Content

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