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

WANG Shan-lei; YUE Kun; WU Hao; TIAN Kai-lin

doi:10.3969/j.issn.1000-5641.2017.05.013

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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 Sciences), 2017, (5): 138-153. doi: 10.3969/j.issn.1000-5641.2017.05.013

Citation:

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 Sciences), 2017, (5): 138-153. doi: 10.3969/j.issn.1000-5641.2017.05.013

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

doi: 10.3969/j.issn.1000-5641.2017.05.013

1.
School of Information Science and Engineering, Yunnan University, Kunming 650500, China
2.
Library of South West Forestry University, Kunming 650224, China

Received Date: 2017-05-01
Publish Date: 2017-09-25

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.
- rating data,
- multi-dimensional preference,
- latent variable,
- Bayesian network,
- Spark

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References(25)

References

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