Integrating Tags in a Semantic Content-based Recommender

Basic content personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, with the attributes of a content object. The Web 2.0 (r)evolution and the advent of user generated content have changed the game for personalization, since the role of people has evolved from passive consumers of information to that of active contributors. One of the forms of user generated content that has drawn more attention from the research community is folksonomy, a taxonomy generated by users who collaboratively annotate and categorize resources of interests with freely chosen keywords called tags. In this paper, we investigate whether folksonomies might be a valuable source of information about user interests. The main contribution is a strategy that enables a content-based recommender to infer user interests by applying machine learning techniques both on the "official" item descriptions provided by a publisher, and on tags which users adopt to freely annotate relevant items. Static content and tags are preventively analyzed by advanced linguistic techniques in order to capture the semantics of the user interests often hidden behind keywords. The proposed approach has been evaluated in the context of cultural heritage personalization. Preliminary experiments involving 30 real users show an improvement in the predictive accuracy of the tag-augmented recommender compared to the pure content-based one.