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Abstract

Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust values among users, termed “web of trust”, allows a twofold enhancement of Recommender Systems. Firstly, the filtering process can be informed by the reputation of users which can be computed by propagating trust. Secondly, the trust metrics can help to solve a problem associated with the usual method of similarity assessment, its reduced computability. An empirical evaluation on Epinions.com dataset shows that trust propagation can increase the coverage of Recommender Systems while preserving the quality of predictions. The greatest improvements are achieved for users who provided few ratings.

Keywords

Recommender System Current User Trust Propagation Collaborative Filter Mean Absolute Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American (2001)Google Scholar
  2. 2.
    Eaton, A.: RVW module for syndicating and aggregating reviews (2004), http://www.pmbrowser.info/rvw/0.2
  3. 3.
    Golbeck, J., Hendler, J., Parsia, B.: Trust networks on the Semantic Web. In: Proceedings of Cooperative Intelligent Agents (2003)Google Scholar
  4. 4.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Communications of the ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  5. 5.
    Guha, R.: Open rating systems. Technical report, Stanford University, CA, USA (2003)Google Scholar
  6. 6.
    Guha, R., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: Proceedings of WWW 2004 (2004)Google Scholar
  7. 7.
    Herlocker, J., Konstan, J., Borchers, J.A., Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. In: Proceedings of the 1999 Conference on Research and Development in Information Retrieval (1999)Google Scholar
  8. 8.
    Levien, R.: Advogato Trust Metric. PhD thesis, UC Berkeley, USA (2003)Google Scholar
  9. 9.
    Massa, P., Bhattacharjee, B.: Using trust in recommender systems: an experimental analysis. In: Proc. of 2nd Int. Conference on Trust Management (2004)Google Scholar
  10. 10.
    O’Mahony, M., Hurley, N., Kushmerick, N., Silvestre, G.: Collaborative recommendation: A robustness analysis. In: Proceedings of Int’l Semantic Web Conf., ISWC 2003 (2003)Google Scholar
  11. 11.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford, USA (1998)Google Scholar
  12. 12.
    Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM 40(3), 56–58 (1997)CrossRefGoogle Scholar
  13. 13.
    Ziegler, C.: Semantic web recommender systems. In: Joint ICDE/EDBT Ph.D. Workshop (2004)Google Scholar
  14. 14.
    Ziegler, C., Lausen, G.: Spreading activation models for trust propagation. In: IEEE International Conference on e-Technology, e-Commerce, and e-Service, EEE 2004 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Paolo Massa
    • 1
  • Paolo Avesani
    • 1
  1. 1.ITC-iRSTPovoItaly

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