Content-based Recommendation Services for Personalized Digital Libraries

This paper describes the possible use of advanced content-based recommendation methods in the area of Digital Libraries. Content-based recommenders analyze documents previously rated by a target user, and build a profile exploited to recommend new interesting documents. One of the main limitations of traditional keyword-based approaches is that they are unable to capture the semantics of the user interests, due to the natural language ambiguity. We developed a semantic recommender system, called ITem Recommender, able to disambiguate documents before using them to learn the user profile. The Conference Participant Advisor service relies on the profiles learned by ITem Recommender to build a personalized conference program, in which relevant talks are highlighted according to the participant's interests.