Recommendations Based on User-Generated Comments in Social Media

Recommender systems gather user profile data either explicitly (users enter it) or implicitly (online behavior tracking).Surprisingly, given the prevalence of social media forums, which contain a rich set of user comments, there have been very few attempts to analyze the content of these comments to build up a user profile. In this paper, we compare and contrast a number of strategies for using text analysis to automatically gather profile data from user comments on news articles. We use this data to prototype a news recommender system based on the Guardian newspaper´s ´Comment is Free´ forum. The paper shows the feasibility of the approach: in a user study with fifty participants, our recommender outperforms a commercial ´best-in-class´ system. Furthermore, we show that user comments allow recommender systems to track an evolving conversation related to a news article and can thus provide recommendations that better match the topics of conversation in comments, which maybe quite different from those in the original news article.