Improved Metric Factorization Recommendation Algorithm Based on Social Networks and Implicit Feedback
The Metric Factorization algorithm solves the problem of the suboptimal solution caused by the inner product of the traditional matrix factorization algorithm. Although the basic metric factorization model has achieved good results in rating prediction and item ranking tasks, the algorithm ignores t...
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| Published in | Journal of physics. Conference series Vol. 1634; no. 1; pp. 12037 - 12042 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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IOP Publishing
01.09.2020
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| Abstract | The Metric Factorization algorithm solves the problem of the suboptimal solution caused by the inner product of the traditional matrix factorization algorithm. Although the basic metric factorization model has achieved good results in rating prediction and item ranking tasks, the algorithm ignores the role of implicit feedback and user social information. Considering the social relationship and implicit feedback information between users, this paper improves the basic metric factor Factorization algorithm, and proposes an improved metric factorization recommendation algorithm based on social networks and implicit feedback. We do rating prediction tasks on the Filmtrust and Last.FM datasets, experimental results show that the improved algorithm can further improve the accuracy of prediction. |
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| AbstractList | The Metric Factorization algorithm solves the problem of the suboptimal solution caused by the inner product of the traditional matrix factorization algorithm. Although the basic metric factorization model has achieved good results in rating prediction and item ranking tasks, the algorithm ignores the role of implicit feedback and user social information. Considering the social relationship and implicit feedback information between users, this paper improves the basic metric factor Factorization algorithm, and proposes an improved metric factorization recommendation algorithm based on social networks and implicit feedback. We do rating prediction tasks on the Filmtrust and Last.FM datasets, experimental results show that the improved algorithm can further improve the accuracy of prediction. Abstract The Metric Factorization algorithm solves the problem of the suboptimal solution caused by the inner product of the traditional matrix factorization algorithm. Although the basic metric factorization model has achieved good results in rating prediction and item ranking tasks, the algorithm ignores the role of implicit feedback and user social information. Considering the social relationship and implicit feedback information between users, this paper improves the basic metric factor Factorization algorithm, and proposes an improved metric factorization recommendation algorithm based on social networks and implicit feedback. We do rating prediction tasks on the Filmtrust and Last.FM datasets, experimental results show that the improved algorithm can further improve the accuracy of prediction. |
| Author | Han, Jiaxin Wang, Bilin Cuan, Ying |
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| References | Srebro (JPCS_1634_1_012037bib1) 2004 Koren (JPCS_1634_1_012037bib2) 2008 Koren (JPCS_1634_1_012037bib5) 2009; 42 Faliagka (JPCS_1634_1_012037bib10) 2011 Zhang (JPCS_1634_1_012037bib3) 2018 Shi (JPCS_1634_1_012037bib7) 2014; 47 He (JPCS_1634_1_012037bib6) 2017 Jamali (JPCS_1634_1_012037bib9) 2010 Gregory (JPCS_1634_1_012037bib4) 2007; 9 (JPCS_1634_1_012037bib12) 1996; 109 Hsieh (JPCS_1634_1_012037bib8) 2017 Hsieh (JPCS_1634_1_012037bib11) 2011 |
| References_xml | – volume: 47 start-page: 1 year: 2014 ident: JPCS_1634_1_012037bib7 article-title: Collaborative filtering beyond the user-item matrix publication-title: Acm Computing Surveys doi: 10.1145/2556270 contributor: fullname: Shi – year: 2004 ident: JPCS_1634_1_012037bib1 article-title: Maximum-Margin Matrix Factorization contributor: fullname: Srebro – start-page: 287 year: 2011 ident: JPCS_1634_1_012037bib11 contributor: fullname: Hsieh – year: 2008 ident: JPCS_1634_1_012037bib2 article-title: Factorization meets the neighborhood: A multifaceted collaborative filtering model doi: 10.1145/1401890.1401944 contributor: fullname: Koren – volume: 9 start-page: 38 year: 2007 ident: JPCS_1634_1_012037bib4 article-title: Interview with simon funk publication-title: acm sigkdd explorations newsletter doi: 10.1145/1294301.1294311 contributor: fullname: Gregory – start-page: 193 year: 2017 ident: JPCS_1634_1_012037bib8 article-title: [ACM Press the 26th International Conference - Perth, Australia (2017.04.03-2017.04.07)] contributor: fullname: Hsieh – start-page: 173 year: 2017 ident: JPCS_1634_1_012037bib6 article-title: [ACM press the 26th international conference - Perth, Australia (2017.04.03-2017.04.07)] contributor: fullname: He – year: 2018 ident: JPCS_1634_1_012037bib3 article-title: Metric factorization: recommendation beyond matrix factorization contributor: fullname: Zhang – start-page: 135 year: 2010 ident: JPCS_1634_1_012037bib9 article-title: A matrix factorization technique with trust propagation for recommendation in social networks contributor: fullname: Jamali – volume: 109 start-page: 38 year: 1996 ident: JPCS_1634_1_012037bib12 article-title: Pearson’s correlation coefficient publication-title: new zealand medical journal – volume: 42 start-page: 30 year: 2009 ident: JPCS_1634_1_012037bib5 article-title: Matrix factorization techniques for recommender systems publication-title: Computer doi: 10.1109/MC.2009.263 contributor: fullname: Koren – year: 2011 ident: JPCS_1634_1_012037bib10 article-title: Teenagers’ Use of Social Network Websites and Privacy Concerns: A Survey doi: 10.1109/PCI.2011.17 contributor: fullname: Faliagka |
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| SubjectTerms | Algorithms Factorization Feedback Physics Social networks |
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| Title | Improved Metric Factorization Recommendation Algorithm Based on Social Networks and Implicit Feedback |
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