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 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.


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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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

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

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