Presentation 2010-11-04
An Application of Generalized Linear Model for Recommender System : Rating Estimation Based on Main-Effect Model
Yu FUJIMOTO,
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Abstract(in English) Collaborative filtering based on a rating matrix is broadly used in recommender systems. In a practical situation, the matrix tends to be sparse when the sets of items and objects are huge. And, sparsity of rating matrices easily deteriorate the accuracy of recommendation. In the regression setup, sparse matrices cause the over-fitting problem, and the number of parameters in a model should be well controlled. In this paper, a simple main-effect model with a small number of parameters is introduced and extended in the framework of the generalized linear model. Even with such a simple model, one can express linearity, independence, and weak special types of non-linearity and dependence between users and objects by introducing a one-parameter family link function. This paper experimentally shows a possibility for improvement of estimation results based on a simple model.
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Keyword(in English) Generalized linear model / collaborative filter / recommender system / linearity, independence
Paper # IBISML2010-68
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Conference Information
Committee IBISML
Conference Date 2010/10/28(1days)
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Paper Information
Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) An Application of Generalized Linear Model for Recommender System : Rating Estimation Based on Main-Effect Model
Sub Title (in English)
Keyword(1) Generalized linear model
Keyword(2) collaborative filter
Keyword(3) recommender system
Keyword(4) linearity, independence
1st Author's Name Yu FUJIMOTO
1st Author's Affiliation Aoyama Gakuin University()
Date 2010-11-04
Paper # IBISML2010-68
Volume (vol) vol.110
Number (no) 265
Page pp.pp.-
#Pages 7
Date of Issue