Semantic network-driven news recommender systems: a celebrity Gossip use case
Marco Fossati, Claudio Giuliano, Giovanni Tummarello · 2012
Abstract. 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 seman-tic 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 recommenda-tions are enhanced by detailed explanations to attract end users ’ atten-tion. An online evaluation with paid workers from crowdsourcing services proves the effectiveness of our approach.