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Recommendations Based on User-Generated Comments in Social Media

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

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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.

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: NLP ; recommender systems ; user-generated content
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 57692
Deposited By: ep_importer_pure
Deposited On: 21 Aug 2012 09:38
Refereed?: No
Published?: Published
Last Modified: 21 Aug 2012 09:38
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/57692

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