Modeling multi-dimensional user preference based on the latent variable model

doi:10.3969/j.issn.1000-5641.2017.05.013

Abstract

Abstract: Modeling user preference from user behavior data is the basis of personalization service, score prediction, user behavior targeting, etc. In this paper, multi-dimensional preferences from rating data are described by multiple latent variables and the Bayesian network with multiple latent variables is adopted as the preliminary knowledge framework of user preference. Constraint conditions are given according to the inherence of user preference and latent variables, upon which we propose a method for modeling user preference. Parameters are computed by EM algorithm and structure is established by SEM algorithm with respect to the given constraints. In the case of multiple latent variables, a large amount of intermediate data is generated in modeling, which causes the increasing computational complexity. Therefore, we implement the modeling method with Spark computing framework. Experiments results on the Movielens dataset verify that the method proposed in this paper is effective.

Key words: rating data, multi-dimensional preference, latent variable, Bayesian network, Spark

CLC Number:

TP311

WANG Shan-lei, YUE Kun, WU Hao, TIAN Kai-lin. Modeling multi-dimensional user preference based on the latent variable model[J]. Journal of East China Normal University(Natural Sc, 2017, 2017(5): 138-153.

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