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FeedbackTrust: Using feedback effects in trust-based recommendation systems

Moghaddam, S ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1145/1639714.1639765
  3. Abstract:
  4. 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
  5. Keywords:
  6. Feedback ; Recommendation ; Trust ; Collaborative filtering ; Data sets ; Feedback effects ; Negative feedback ; Online social networks ; Positive feedback ; Prediction errors ; Recommendation systems ; Social networks ; Trust models ; Trust networks ; Trust values ; User feedback ; Behavioral research
  7. Source: 3rd ACM Conference on Recommender Systems, RecSys'09, New York, NY, 23 October 2009 through 25 October 2009 ; 2009 , Pages 269-272 ; 9781605584355 (ISBN)
  8. URL: https://dl.acm.org/citation.cfm?id=1639765