Neural Information Processing - Letters and Reviews

Vol. 12, Nos. 1-3, January-March 2008

 

pp. 11-20

 

α-Bayesian Collaboration of Multiple Predictors and its Applications to Hybrid Recommendation and User Modeling

 

Jun-ichiro Hirayama, Takashi Takenouchi

Graduate School of Information Science, Nara Institute of Science and Technology,

Takayama 8916-5, Ikoma, Nara

{junich-h, ttakashi}@is.naist.jp

 

Masashi Nakatomi

Ricoh Company, Ltd.

nakatomi@rdc.ricoh.co.jp

 

Shin Ishii

Graduate School of Informatics, Kyoto University

ishii@i.kyoto-u.ac.jp

 

Abstract

In recent widespread areas of machine learning or data mining applications, one often should deal with multiple statistical prediction tasks simultaneously, each of which cannot be successful by itself due to limitation of data amount. Among several approaches to utilizing task relationship, a naive but still important approach is to separately train taskspecific predictors in advance and then integrate them at the time of prediction. In this study, we propose a general framework of realizing such a “collaborative prediction” mechanism, specifically based on an existing generalization of Bayesian predictive distribution using the α-divergence. We also propose a novel hybrid method of collaborative and content-based recommendations, under the proposed framework. We demonstrate the effectiveness of the proposed method by using two kinds of real datasets; one is the Movielens benchmark collection related to recommendation, and the other is a real log dataset of shared electronic devices related to a particular problem of user modeling.

 

Keywords – Bayesian predictive distribution, Collaborative prediction, Movielens benchmark