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Developing Trust Networks Based on User Tagging Information for Recommendation Making

  • Touhid Bhuiyan
  • Yue Xu
  • Audun Jøsang
  • Huizhi Liang
  • Clive Cox
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6488)

Abstract

Recommender systems are one of the recent inventions to deal with ever growing information overload. Collaborative filtering seems to be the most popular technique in recommender systems. With sufficient background information of item ratings, its performance is promising enough. But research shows that it performs very poor in a cold start situation where previous rating data is sparse. As an alternative, trust can be used for neighbor formation to generate automated recommendation. User assigned explicit trust rating such as how much they trust each other is used for this purpose. However, reliable explicit trust data is not always available. In this paper we propose a new method of developing trust networks based on user’s interest similarity in the absence of explicit trust data. To identify the interest similarity, we have used user’s personalized tagging information. This trust network can be used to find the neighbors to make automated recommendations. Our experiment result shows that the proposed trust based method outperforms the traditional collaborative filtering approach which uses users rating data. Its performance improves even further when we utilize trust propagation techniques to broaden the range of neighborhood.

Keywords

Trust Networks Interest Similarity Recommender Systems Social Networks and Tag 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Touhid Bhuiyan
    • 1
  • Yue Xu
    • 1
  • Audun Jøsang
    • 2
  • Huizhi Liang
    • 1
  • Clive Cox
    • 3
  1. 1.Faculty of Science and TechnologyQueensland University of TechnologyAustralia
  2. 2.UniK Graduate SchoolUniversity of OsloNorway
  3. 3.Rummble.comCambridgeEngland

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