Abstract Virtual communities are gradually becoming an inherent part of modern life. One major challenge of these communities is to provide their users with a reliable environment in which they can interact with each other. Trust and reputation systems, which are considered key enablers of virtual communities, attempt to contend with this challenge. These systems support the accumulation of member reputation information and leverage this information to increase the likelihood of successful member interactions and to better protect the community from fraudulent members. Reputation information is an important part of a user’s identity, which makes this data both sensitive and desired for communities to share. Reputation sharing is also motivated by the individual user that can leverage her state in new communities using the reputation she gained in others. In previous work we have presented a comprehensive model for computing reputation called the Knots model, which enables users to customize reputation computation according to their group-related characteristics. We also presented a model named Cross-Community Reputation (CCR) model for sharing reputation knowledge among communities. The CCR model identifies the fundamental terms required for a meaningful sharing of reputation information among communities and proposes means to make the information sharing feasible. After reviewing the work on the Knots and CCR models this presentation discusses TRIC - a Trust and Reputation infra-structure for Virtual communities. The major concerns of TRIC are to protect the user’s rights for privacy and control over her data and to draw some architecture guidelines that can be implemented in more than one way. We explain the major components required for this type of infrastructure and discuss known design alternatives and their tradeoffs in the case of TRIC.
Welcome!
Hannu Toivonen
PS. Prof. Gudes will visit the Department of Computer Science from May 17th to 29th. He will also give a short course on Graph and Link Mining, see http://www.cs.helsinki.fi/hecse/courses/Graph_Mining_2010/