A Hybrid Content-Collaborative Recommender System Integrated into an Electronic Performance Support System

An electronic performance support system (EPSS) introduces challenges on contextualized and personalized information delivery. Recommender systems aim at delivering and suggesting relevant information according to users preferences, thus EPSSs could take advantage of the recommendation algorithms that have the effect of guiding users in a large space of possible options. The JUMP project aims at integrating an EPSS with a hybrid recommender system. Collaborative and content-based filtering are the recommendation techniques most widely adopted to date. The main contribution of this paper is a content- collaborative hybrid recommender which computes similarities between users relying on their content- based profiles, in which user preferences are stored, instead of comparing their rating styles. A distinctive feature of our system is that a statistical model of the user interests is obtained by machine learning techniques integrated with linguistic knowledge contained in WordNet. This model, named "semantic user profile", is exploited by the hybrid recommender in the neighborhood formation process.