Social Recommendation Based on Quantified Trust and User’s Primary Preference Space
Social recommendation has received great attention recently, which uses social information to alleviate the data sparsity problem and the cold-start problem of recommendation systems. However, the existing social recommendation methods have two deficiencies. First, the binary trust network used by c...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/23/12141 |
| _version_ | 1797463620998660096 |
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| author | Suqi Zhang Ningjing Zhang Ningning Li Zhijian Xie Junhua Gu Jianxin Li |
| author_facet | Suqi Zhang Ningjing Zhang Ningning Li Zhijian Xie Junhua Gu Jianxin Li |
| author_sort | Suqi Zhang |
| collection | DOAJ |
| description | Social recommendation has received great attention recently, which uses social information to alleviate the data sparsity problem and the cold-start problem of recommendation systems. However, the existing social recommendation methods have two deficiencies. First, the binary trust network used by current social recommendation methods cannot reflect the trust level of different users. Second, current social recommendation methods assume that users only consider the same influencial factors when purchasing goods and establishing friendships, which does not match the reality, since users may have different preferences in different scenarios. To address these issues, in this paper, we propose a novel social recommendation framework based on trust and preference, named TPSR, including a trust quantify method based on random walk with restart (TQ_RWR) and a user’s primary preference space model (UPPS). Our experimental results in four public real-world datasets show that TQ_RWR can improve the utilization of trust information, and improve the recommended accuracy. In addition, compared with current social recommendation methods/studies, TPSR can achieve a higher performance in different metrics, including root mean square error, precision, recall and F1 value. |
| first_indexed | 2024-03-09T17:53:22Z |
| format | Article |
| id | doaj.art-7a38faffbedc4edb961e5bf7503454d1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:53:22Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7a38faffbedc4edb961e5bf7503454d12023-11-24T10:31:39ZengMDPI AGApplied Sciences2076-34172022-11-0112231214110.3390/app122312141Social Recommendation Based on Quantified Trust and User’s Primary Preference SpaceSuqi Zhang0Ningjing Zhang1Ningning Li2Zhijian Xie3Junhua Gu4Jianxin Li5School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Science, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300401, ChinaSchool of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300401, ChinaSchool of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300401, ChinaSchool of IT, Deakin University, Burwood, VIC 3125, AustraliaSocial recommendation has received great attention recently, which uses social information to alleviate the data sparsity problem and the cold-start problem of recommendation systems. However, the existing social recommendation methods have two deficiencies. First, the binary trust network used by current social recommendation methods cannot reflect the trust level of different users. Second, current social recommendation methods assume that users only consider the same influencial factors when purchasing goods and establishing friendships, which does not match the reality, since users may have different preferences in different scenarios. To address these issues, in this paper, we propose a novel social recommendation framework based on trust and preference, named TPSR, including a trust quantify method based on random walk with restart (TQ_RWR) and a user’s primary preference space model (UPPS). Our experimental results in four public real-world datasets show that TQ_RWR can improve the utilization of trust information, and improve the recommended accuracy. In addition, compared with current social recommendation methods/studies, TPSR can achieve a higher performance in different metrics, including root mean square error, precision, recall and F1 value.https://www.mdpi.com/2076-3417/12/23/12141recommendation systemcollaborative filteringtrust networkmatrix factorization |
| spellingShingle | Suqi Zhang Ningjing Zhang Ningning Li Zhijian Xie Junhua Gu Jianxin Li Social Recommendation Based on Quantified Trust and User’s Primary Preference Space Applied Sciences recommendation system collaborative filtering trust network matrix factorization |
| title | Social Recommendation Based on Quantified Trust and User’s Primary Preference Space |
| title_full | Social Recommendation Based on Quantified Trust and User’s Primary Preference Space |
| title_fullStr | Social Recommendation Based on Quantified Trust and User’s Primary Preference Space |
| title_full_unstemmed | Social Recommendation Based on Quantified Trust and User’s Primary Preference Space |
| title_short | Social Recommendation Based on Quantified Trust and User’s Primary Preference Space |
| title_sort | social recommendation based on quantified trust and user s primary preference space |
| topic | recommendation system collaborative filtering trust network matrix factorization |
| url | https://www.mdpi.com/2076-3417/12/23/12141 |
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