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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

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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
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
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:
DateEvent
24 November 2017Available
30 October 2017Accepted
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:
Project/Grant IDRIOXX Funder NameFunder ID
690907[ERC] Horizon 2020 Framework Programmehttp://dx.doi.org/10.13039/100010661

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