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Matrix factorization with rating completion : an enhanced SVD Model for collaborative filtering recommender systems
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Xin, Guan, Li, Chang-Tsun and Yu, Guan (2017) Matrix factorization with rating completion : an enhanced SVD Model for collaborative filtering recommender systems. IEEE Access, 5 . pp. 27668-27678. doi:10.1109/ACCESS.2017.2772226 ISSN 2169-3536.
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Official URL: https://doi.org/10.1109/ACCESS.2017.2772226
Abstract
Collaborative filtering algorithms, such as matrix factorization techniques, are recently gaining momentum due to their promising performance on recommender systems. However, most collaborative filtering algorithms suffer from data sparsity. Active learning algorithms are effective in reducing the sparsity problem for recommender systems by requesting users to give ratings to some items when they enter the systems. In this paper, a new matrix factorization model, called Enhanced SVD (ESVD) is proposed, which incorporates the classic matrix factorization algorithms with ratings completion inspired by active learning. In addition, the connection between the prediction accuracy and the density of matrix is built to further explore its potentials. We also propose the Multi-layer ESVD, which learns the model iteratively to further improve the prediction accuracy. To handle the imbalanced data sets that contain far more users than items or more items than users, the Item-wise ESVD and User-wise ESVD are presented, respectively. The proposed methods are evaluated on the famous Netflix and Movielens data sets. Experimental results validate their effectiveness in terms of both accuracy and efficiency when compared with traditional matrix factorization methods and active learning methods.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Algorithms, Matrices, Matrix analytic methods , Recommender systems (Information filtering) | ||||||
| Journal or Publication Title: | IEEE Access | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 2169-3536 | ||||||
| Official Date: | 24 November 2017 | ||||||
| Dates: |
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| Volume: | 5 | ||||||
| Page Range: | pp. 27668-27678 | ||||||
| DOI: | 10.1109/ACCESS.2017.2772226 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 24 July 2018 | ||||||
| Date of first compliant Open Access: | 30 July 2018 | ||||||
| RIOXX Funder/Project Grant: |
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