A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context
Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation syst...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/7070487 |
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| _version_ | 1849308323680092160 |
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| author | Guangxia Xu Zhijing Tang Chuang Ma Yanbing Liu Mahmoud Daneshmand |
| author_facet | Guangxia Xu Zhijing Tang Chuang Ma Yanbing Liu Mahmoud Daneshmand |
| author_sort | Guangxia Xu |
| collection | DOAJ |
| description | Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time. |
| format | Article |
| id | doaj-art-0e01f55fbf40442e8c72aaabfbb67aed |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-0e01f55fbf40442e8c72aaabfbb67aed2025-08-20T03:54:29ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552019-01-01201910.1155/2019/70704877070487A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time ContextGuangxia Xu0Zhijing Tang1Chuang Ma2Yanbing Liu3Mahmoud Daneshmand4Department of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaDepartment of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaDepartment of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaDepartment of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaDepartment of Business Intelligence & Analytics, Stevens Institute of Technology, Hoboken 07030, USAComplex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time.http://dx.doi.org/10.1155/2019/7070487 |
| spellingShingle | Guangxia Xu Zhijing Tang Chuang Ma Yanbing Liu Mahmoud Daneshmand A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context Journal of Electrical and Computer Engineering |
| title | A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context |
| title_full | A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context |
| title_fullStr | A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context |
| title_full_unstemmed | A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context |
| title_short | A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context |
| title_sort | collaborative filtering recommendation algorithm based on user confidence and time context |
| url | http://dx.doi.org/10.1155/2019/7070487 |
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