Abstract
User profiles are the base to obtain knowledge about users of recommender systems. We propose a context- and social-aware user profiling for audiovisual recommender systems that combines explicit preferences, implicit preferences and stereotypes modeling, taking advantage of information available in social networks and the current user context. We examine how the user profile is represented, acquired, built and updated; and how the profile information is exploited by an audiovisual recommender system that uses both collaborative filtering and the content-based method.
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Mantilla, C.A., Torres-Padrosa, V., Fabregat, R. (2013). Context- and Social-Aware User Profiling for Audiovisual Recommender Systems. In: Omatu, S., Neves, J., Rodriguez, J., Paz Santana, J., Gonzalez, S. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-00551-5_68
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DOI: https://doi.org/10.1007/978-3-319-00551-5_68
Publisher Name: Springer, Cham
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