Recommendations based on user-generated comments in social media

Andrew Massenger, Jon Whittle

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

9 Citations (Scopus)

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 may be quite different from those in the original news article.

Original languageEnglish
Title of host publicationProceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages505-508
Number of pages4
ISBN (Print)9780769545783
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 IEEE International Conference on Privacy, Security, Risk and Trust and 2011 IEEE International Conference on Social Computing - Boston, United States of America
Duration: 9 Oct 201111 Oct 2011

Conference

Conference2011 IEEE International Conference on Privacy, Security, Risk and Trust and 2011 IEEE International Conference on Social Computing
Abbreviated titlePASSAT 2011 & SocialCom 2011
Country/TerritoryUnited States of America
CityBoston
Period9/10/1111/10/11

Keywords

  • NLP
  • Recommender systems
  • User-generated content

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