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Increasing prediction accuracy in collaborative filtering with initialized factor matrices

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Abstract

Recommender systems are useful tools to give personalized recommendations to users. One of the most popular techniques used in these systems is collaborative filtering. Recommender system algorithms get into trouble with data sparsity and scalability. These challenges cause lack of convergence in our algorithms. In this research, we propose a new method based on matrix factorization which alleviates data sparsity. We suggest a new method which can be performed as a preprocessing method for initial latent factor matrices of users and items. Initialized latent factors in matrix factorization lead to two advantages: (1) sparsity and scalability would be covered and (2) convergence of algorithms would be faster. We have shown that our method has improved the accuracy of optimization-based matrix factorization technique. Also it has increased the speed of matrix factorization convergence.

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Authors and Affiliations

  1. School of Computer engineering, Iran University of Science and Technology, Tehran, Iran

    Mahdi Nasiri & Behrouz Minaei

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  1. Mahdi Nasiri
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  2. Behrouz Minaei
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Correspondence to Behrouz Minaei.

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Nasiri, M., Minaei, B. Increasing prediction accuracy in collaborative filtering with initialized factor matrices. J Supercomput 72, 2157–2169 (2016). https://doi.org/10.1007/s11227-016-1717-8

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  • DOI: https://doi.org/10.1007/s11227-016-1717-8

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