Research on Hybrid Collaborative Filtering Recommendation Algorithm Based on the Time Effect and Sentiment Analysis
In this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor (ITWCF). This algorithm considers information influence attenuation over time, introduces a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6635202 |
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| _version_ | 1850158491601207296 |
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| author | Xibin Wang Zhenyu Dai Hui Li Jianfeng Yang |
| author_facet | Xibin Wang Zhenyu Dai Hui Li Jianfeng Yang |
| author_sort | Xibin Wang |
| collection | DOAJ |
| description | In this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor (ITWCF). This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half-value period, and proposes a time-weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. In addition, to further improve the quality of the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users’ sentiment tendency by analysis of user review features is proposed to mine users’ attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K-means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model (TWCHR) based on the improved K-means clustering algorithm is then proposed, by combining item-based and user-based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model. |
| format | Article |
| id | doaj-art-5fcf45d8253d4ebcafb3c76e8ba21060 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-5fcf45d8253d4ebcafb3c76e8ba210602025-08-20T02:23:51ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66352026635202Research on Hybrid Collaborative Filtering Recommendation Algorithm Based on the Time Effect and Sentiment AnalysisXibin Wang0Zhenyu Dai1Hui Li2Jianfeng Yang3School of Data Science, Guizhou Institute of Technology, Guiyang 550003, Guizhou, ChinaCollege of Computer Science & Technology, Guizhou University, Guiyang 550025, Guizhou, ChinaCollege of Computer Science & Technology, Guizhou University, Guiyang 550025, Guizhou, ChinaSchool of Data Science, Guizhou Institute of Technology, Guiyang 550003, Guizhou, ChinaIn this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor (ITWCF). This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half-value period, and proposes a time-weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. In addition, to further improve the quality of the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users’ sentiment tendency by analysis of user review features is proposed to mine users’ attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K-means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model (TWCHR) based on the improved K-means clustering algorithm is then proposed, by combining item-based and user-based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model.http://dx.doi.org/10.1155/2021/6635202 |
| spellingShingle | Xibin Wang Zhenyu Dai Hui Li Jianfeng Yang Research on Hybrid Collaborative Filtering Recommendation Algorithm Based on the Time Effect and Sentiment Analysis Complexity |
| title | Research on Hybrid Collaborative Filtering Recommendation Algorithm Based on the Time Effect and Sentiment Analysis |
| title_full | Research on Hybrid Collaborative Filtering Recommendation Algorithm Based on the Time Effect and Sentiment Analysis |
| title_fullStr | Research on Hybrid Collaborative Filtering Recommendation Algorithm Based on the Time Effect and Sentiment Analysis |
| title_full_unstemmed | Research on Hybrid Collaborative Filtering Recommendation Algorithm Based on the Time Effect and Sentiment Analysis |
| title_short | Research on Hybrid Collaborative Filtering Recommendation Algorithm Based on the Time Effect and Sentiment Analysis |
| title_sort | research on hybrid collaborative filtering recommendation algorithm based on the time effect and sentiment analysis |
| url | http://dx.doi.org/10.1155/2021/6635202 |
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