A Logistic Factorization Model for Recommender Systems With Multinomial Responses

Yu Wang, Xuan Bi, Annie Qu

Research output: Contribution to journalArticle

Abstract

In this article, we propose a two-way multinomial logistic model for recommender systems for categorical ratings. Specifically, we treat the possible ratings as mutually exclusive events, whose probability is determined by the latent factor of the users and the items through a two-way multinomial logistic function. The proposed method has a compatibility with categorical ratings and the advantage of incorporating both the covariate information and the latent factors of the users and items uniformly. We show numerically that the proposed method performs consistently better than five commonly used collaborative filtering methods, namely, the restricted singular value decomposition, the soft-impute matrix completion method, the regression-based latent factor models, the restricted Boltzmann machine, and the group-specific recommender system on various simulation setups and on MovieLens data. Supplementary materials for this article are available online.

Original languageEnglish (US)
JournalJournal of Computational and Graphical Statistics
DOIs
StateAccepted/In press - Jan 1 2019

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Recommender Systems
Logistics
Factorization
Categorical
Matrix Completion
Boltzmann Machine
Multinomial Model
Mutually exclusive
Collaborative Filtering
Logistic Model
Factor Models
Singular value decomposition
Model
Compatibility
Covariates
Regression
Recommender systems
Rating
Simulation
Latent factors

Keywords

  • Cold-start problem
  • Collaborative filter
  • MovieLens

Cite this

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