Presentation 2010-01-19
An Extension of Matrix Factorization to Mixture Model and an Application to Prediction of Movei Ratings
Masayoshi NAKAMURA, Takashi TAKENOUCHI, Kazushi IKEDA,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) Recommendation systems suggest items to an user, based on existing history of evaluation for items by the users. In many cases, history information of users is represented as a matrix form, in which most part are missing. Matrix factorization is a well-known method for predicting missing values of the sparse matrix. The cost function of the conventional matrix factorization corresponds to the log-likelihood with a single Gaussian model, whose mean is assumed to be factorizes. However, in real-world datasets, a group of users forms a cluster and each cluster has different characteristic. For the situation the model of single Gaussian is not appropriate. To tackle the situation, in this report, we extend matrix factorization with a mixture model, and investigate our method using a synthetic dataset and a real-world dataset.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Matrix factorization / Mixture model / Missing value prediction / Probabilistic model
Paper # NC2009-86
Date of Issue

Conference Information
Committee NC
Conference Date 2010/1/11(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) An Extension of Matrix Factorization to Mixture Model and an Application to Prediction of Movei Ratings
Sub Title (in English)
Keyword(1) Matrix factorization
Keyword(2) Mixture model
Keyword(3) Missing value prediction
Keyword(4) Probabilistic model
1st Author's Name Masayoshi NAKAMURA
1st Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology()
2nd Author's Name Takashi TAKENOUCHI
2nd Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology
3rd Author's Name Kazushi IKEDA
3rd Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology
Date 2010-01-19
Paper # NC2009-86
Volume (vol) vol.109
Number (no) 363
Page pp.pp.-
#Pages 5
Date of Issue