FeedbackTrust: using feedback effects in trust-based recommendation systems

ABSTRACT

With the advent of online social networks, the trust-based approach to recommendation has emerged which exploits the trust network among users and makes recommendations based on the ratings of trusted users in the network. In this paper, we introduce a two dimensional trust model which dynamically gets updated based on users's feedbacks, in contrast to static trust values in current trust models. Explorability measures the extent to which a user can rely on recommendations returned by the social network of a trusted user. Dependability represents the extent to which a user's own ratings can be trusted by users trusting him directly and indirectly. We propose a method to learn the values of explorability and dependability from raw trust data and feedback expressed by users on the recommendations they receive. Positive feedback will increase the trust and negative feedback will decrease the trust among users. We performed an evaluation on the Epinions dataset, demonstrating that exploiting user feedback results in lower prediction error compared to existing trust-based and collaborative filtering approaches.

REFERENCES

• 1: A. Abdul-Rahman and S. Hailes. Supporting trust in virtual communities. In Proceedings of the 33rd Hawaii International Conference on System Sciences, USA, 2000.
• 2: J. Golbeck. Generating predictive movie recommendations from trust in social networks. In Proceedings of The Fourth International Conference on Trust Management, 2006.
• 3: C. M. Jonker and J. Treur. Formal analysis of models for the dynamics of trust based on experiences. In Proceedings of the 9th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, London, UK, 1999.
• 4: P. Massa and P. Avesani. Trust-aware recommender systems. In RecSys'07, Minneapolis, Minnesota, USA, 2007.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Relevance feedback

General Terms:
Algorithms, Experimentation

Keywords:
feedback, recommendation, teust