Semantic Network-driven News Recommender Systems: a Celebrity Gossip Use Case.

Information overload on the Internet motivates the need for filtering tools. Recommender systems play a significant role in such a scenario, as they provide automatically generated suggestions. In this paper, we propose a novel recommendation approach, based on semantic networks exploration. Given a set of celebrity gossip news articles, our systems leverage both natural language processing text annotation techniques and knowledge bases. Hence, real-world entities detection and cross-document entity relations discovery are enabled. The recommendations are enhanced by detailed explanations to attract end users' attention. An online evaluation with paid workers from crowdsourcing services proves the effectiveness of our approach.