Elastic net-regularized latent factor model for recommender systems
2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC)(2018)
摘要
Latent factor (LF) models are highly efficient in recommender systems. The problem of LF analysis 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. Hence, regularization schemes are vital in improving the generality of an LF model. This work innovatively applies the elastic net-based regularization to an LF model defined on HiDS matrices. To do so, we have 1) adapted the elastic net-based regularization scheme to an LF model to fit the sparsity of an HiDS matrix; and 2) designed feasible and efficient algorithm for an LF model with elastic net-based regularization. Experimental results on two large, real datasets show that with properly-tuned and elastic net-based regularization, the resultant model achieves 1) high prediction accuracy for missing data in an HiDS matrix; 2) high computational efficiency; and 3) sparse LF distribution.
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关键词
High-dimensional and Sparse Matrix,Latent Factor Analysis,Elastic Net-based Regularization,Sparse Model
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