A generative graphical model for collaborative filtering of visual content

Authors:
Sabri Boutemedjet;Djemel Ziou
Affiliations:
DI, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada;DI, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
Venue:
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Year:
2006

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Abstract

In this paper, we propose a novel generative graphical model for collaborative filtering of visual content. The preferences of the ”like-minded” users are modelled in order to predict the relevance of visual documents represented by their visual features. We formulate the problem using a probabilistic latent variable model where user's preferences and items' classes are combined into a unified framework in order to provide an accurate and a generative model that overcomes the new item problem, generally encountered in traditional collaborative filtering systems.