Stories Around You - A Two-Stage Personalized News Recommendation

With the tremendous growth of published news articles, a key issue is how to help users find diverse and interesting news stories. To this end, it is crucial to understand and build accurate profiles for both users and news articles. In this paper, we define a user profile based on (1) the set of entities she/he talked about it in her/his comments and (2) the set of key-concepts related to those entities on which the user has expressed an opinion or a viewpoint. The same information is extracted from the content of each news article to create its profile. In a first step, we matched those profiles using a new similarity measure. We use also the news articles profiles to diversify the list of recommended stories in a second step. A first evaluation involving the activities of 150 real users in four news web sites, namely The Independent, The Telegraph, CNN and Aljazeera has shown the effectiveness of our approach compared to recent works.