Messenger, A. and Whittle, J. (2011) Recommendations Based on User-Generated Comments in Social Media. In: Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom). IEEE, pp. 505-508. ISBN 978-1-4577-1931-8
Full text not available from this repository.Abstract
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.