gSVD++: supporting implicit feedback on recommender systems with metadata awareness

Authors:
Marcelo Garcia Manzato
Affiliations:
University of São Paulo, São Carlos, SP, Brazil
Venue:
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Year:
2013

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Abstract

This paper proposes a recommender algorithm denominated "gSVD++" which exploits implicit feedback from users by considering not only the latent space of factors describing the user and item, but also the available metadata associated to the content. Such descriptions are an important source to construct a user profile containing relevant and meaningful information about his/her preferences. The method is evaluated on the MovieLens dataset, being compared against other approaches reported in the literature. The results show the effectiveness of incorporating metadata awareness into a latent factor model.