Nowadays, more and more people are using online news platforms as their main source of information about daily life events. Users of such platforms discuss around topics providing new insights and sometimes revealing hidden aspects about topics. The valuable information provided by users needs to be exploited to improve the accuracy of news recommendation and thus keep users always motivated to provide comments. However, exploiting user generated content is very challenging due its noisy nature. In this paper, we address this problem by proposing a novel news recommendation system that (1) enrich the profile of news article with user generated content, (2) deal with noisy contents by proposing a ranking model for users’ comments, and (3) propose a diversification model for comments to remove redundancies and provide a wide coverage of topic aspects. The results show that our approach outperforms baseline approaches achieving high accuracy.