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Elastic-net regularized latent factor analysis-based models for recommender systems
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DOI：10.1016/j.neucom.2018.10.046.png)
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Latent factor analysis (LFA)-based models are highly efficient in recommender systems. The problem of LFA is defined on high-dimensional and sparse (HiDS) matrices corresponding to relationships among numerous entities in industrial applications. It is ill-posed without a unique and optimal solution, making regularization vital in improving the generality of an LFA-based model. Current models mostly adopt l(2) norm-based regularization, which cannot regularize the latent factor distributions. For addressing this issue, this work applies the elastic-net-based regularization to an LFA-based model, thereby achieving an elastic-net regularized latent factor analysis-based (ERLFA) model. We further adopt two efficient learning algorithms, i.e., forward-looking sub-gradients and forward-backward splitting and stochastic proximal gradient descent, to train desired latent factors in an ERLFA-based model, resulting in two novel ERLFA-based models relying on different learning schemes. Experimental results on four large industrial datasets show that by regularizing the latent factor distribution, the proposed ERLFA-based models are able to achieve high prediction accuracy for missing data of an HiDS matrix without additional computational burden. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Big data
Recommender systems
Collaborative filtering
Latent factor analysis
Elastic-net
Regularization
Latent factor distribution
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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