Exploiting Big Data for Enhanced Representations in Content-Based Recommender Systems

The recent explosion of Big Data is offering new chances and challenges to all those platforms that provide personalized access to information sources, such as recommender systems and personalized search engines. In this context, social networks are gaining more and more interests since they represent a perfect source to trigger personalization tasks. Indeed, users naturally leave on these platforms a lot of data about their preferences, feelings, and friendships. Hence, those data are really valuable for addressing the cold start problem of recommender systems. On the other hand, since content shared on social networks is noisy and heterogeneous, information extracted must be hardly processed to build user profiles that can effectively mirror user interests and needs. In this paper we investigated the effectiveness of external knowledge derived from Wikipedia in representing both documents and user profiles in a recommendation scenario. Specifically, we compared a classical keyword-based representation with two techniques that are able to map unstructured text with Wikipedia pages. The advantage of using this representation is that documents and user profiles become richer, more human-readable, less noisy, and potentially connected to the Linked Open Data (LOD) cloud. The goal of our preliminary experimental evaluation was twofolds: 1) to define the representation that best reflects user preferences; 2) to define the representation that provides the best predictive accuracy. We implemented a news recommender for a preliminary evaluation of our model. We involved more than 50 Facebook and Twitter users and we demonstrated that the encyclopedic-based representation is an effective way for modeling both user profiles and documents.