Hierarchical naive bayes models for representing user profiles

In this paper, we show how a user profile can be enhanced when a more detailed description of the products is included. Two main assumptions have been considered: the first implies that the set of features used to describe an item can be organized into a well-defined set of components or categories, and the second is that the user's rating for a given item is obtained by combining user opinions of the relevance of each component. Categories and Subject Descriptors H.3 [Information Storage and Retrieval] General Terms Algorithms Keywords Probabilistic Reasoning, Recommender System, Recommender Systems, Learning User Profiles

• Type: Journal
• Year: 2008
• Where: SIGIR
• Authors: Juan F. Huete, Luis M. de Campos, Juan M. Fernández-Luna, Miguel A. Rueda-Morales
• Keywords: Information Technology, Keywords Probabilistic Reasoning, Recommender Systems, SIGIR 2008, User Profiles