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.
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Boutemedjet, S., Ziou, D. (2006). A Generative Graphical Model for Collaborative Filtering of Visual Content. In: Perner, P. (eds) Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining. ICDM 2006. Lecture Notes in Computer Science(), vol 4065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11790853_32
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DOI: https://doi.org/10.1007/11790853_32
Publisher Name: Springer, Berlin, Heidelberg
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