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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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1634; no. 1; pp. 12037 - 12042
Main Authors Wang, Bilin, Han, Jiaxin, Cuan, Ying
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.09.2020
Subjects
Algorithms
Factorization
Feedback
Physics
Social networks
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Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1634/1/012037