Folksonomy-boosted social media search and ranking

  • Authors:

  • Majdi Rawashdeh;Heung-Nam Kim;Abdulmotaleb El Saddik

  • Affiliations:

  • University of Ottawa, King Edward, Ottawa, Ontario, Canada;University of Ottawa, King Edward, Ottawa, Ontario, Canada;University of Ottawa, King Edward, Ottawa, Ontario, Canada

  • Venue:
  • Proceedings of the 1st ACM International Conference on Multimedia Retrieval
  • Year:
  • 2011

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Abstract

With the rapid proliferation of social media services, users on the social Web are overwhelmed by the huge amount of social media available. In this paper, we look into the potential of social tagging in social media services to help users in retrieving social media. By leveraging social tagging, we propose a new personalized search method to enhance not only retrieval accuracy but also retrieval coverage. Our approach first determines the similarities between resources and between tags. Thereafter, we build two models: a user-tag relation model that reflects how a certain user has assigned tags similar to a given tag and a tag-item relation model that captures how a certain tag has been tagged to resources similar to a given resource. We then seamlessly map the tags on the items depending on a particular user's query in order to find the most attractive media content relevant to the user needs. The experimental evaluations have shown the proposed method achieves better search results than state-of-the art algorithms in terms of accuracy and coverage.