2006 | OriginalPaper | Buchkapitel
A Generative Graphical Model for Collaborative Filtering of Visual Content
verfasst von : Sabri Boutemedjet, Djemel Ziou
Erschienen in: Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Verlag: Springer Berlin Heidelberg
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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.