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Lookup NU author(s): Dr Yu GuanORCiD
This is the final published version of an article that has been published in its final definitive form by IEEE, 2017.
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OAPA 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 (MESVD), which learns the model iteratively to further improve the prediction accuracy. To handle the imbalanced datasets that contain far more users than items or more items than users, the Item-wise ESVD (IESVD) and User-wise ESVD (UESVD) are presented, respectively. The proposed methods are evaluated on the famous Netflix and Movielens datasets. Experimental results validate their effectiveness in terms of both accuracy and efficiency when compared with traditional matrix factorization methods and active learning methods.
Author(s): Guan X, Li C, Guan Y
Publication type: Article
Publication status: Published
Journal: IEEE Access
Year: 2017
Volume: 5
Pages: 27668-27678
Online publication date: 24/11/2017
Acceptance date: 02/04/2016
Date deposited: 18/12/2017
ISSN (electronic): 2169-3536
Publisher: IEEE
URL: https://doi.org/10.1109/ACCESS.2017.2772226
DOI: 10.1109/ACCESS.2017.2772226
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