Fuzzy Trust Inference in Trust Graphs and its Application in Semantic Web Social Networks

People generate information or get it from the others. When one gets information from the others it is important to get it from trusted ones. Each individual in a society can get the information he needs form his trusted friends but there are also many other people in the society that he or she indirectly trusts and can benefit from their information. The idea of benefiting from the indirectly trusted people can well be employed in social networks where finding trusted people can be automated. There should be a feature for users to specify how much they trust a friend and a mechanism to infer the trust in the society trust graph from one user to another that is not directly a friend of the user so that a recommender site can benefit from these inferred trust ratings for showing trustworthy information to each user from her or his point of view from not only her or his directly trusted friends but also the other indirectly trusted users. A problem that is faced in inference in such a large network is contradictory information. This work suggests using fuzzy linguistic terms to specify trust to other users and proposes an algorithm for inferring trust from a person to another person that may be not directly connected in the trust graph of a social network. The algorithm is implemented and compared to the previous one that models trust as numbers in a range. While according to the imprecise nature of trust concept, writing and reading a linguistic expression for trust is much more natural than a number for users, the fuzzy composing strategy performs better than the previous algorithm in integrating conflicting beliefs and conveying the conflict in the inferred result.