Similarity-based Techniques for Trust Management

A network of people having established trust relations and a model for propagation of related trust scores are fundamental building blocks in many of todayŠs most successful e-commerce and recommendation systems. Many online communities are only successful if sufficient mutual trust between their members exists. Users want to know whom to trust and how much to trust in the competence and benevolence of other community members in a specific application domain. However, the web of trust is often too sparse to predict trust values between non-familiar people with high accuracy. Since, at least in large online communities, a user has experience with only a very small fraction of the other community members. Thus, very often there will be no trust relation to an intended new partner of an e-commerce transaction at all. The process of building trust is hereby performed in two different ways. First, one can establish trust (or distrust) by gaining direct experience with another party. Of course, every positive event increases the assumed trustworthiness of the trustee while every negative one reduces it. Second, one can gain trust based on recommendations of third parties. If, e.g., Alice has high trust in Bob’s ability to assess the trustworthiness of other people, Bob has similar trust in Claire’s recommendations, and Claire considers David trustable based on her personal experience with him, then Alice gains also trust in David even if she has no or very limited knowledge of him at all. This form of propagated trust is called trust transitivity. Trust transitivity may provide additional information to alleviate the consequences of the sparsity and possible cold-start problems. Such approaches are helpful, provided that a complete trust path exists between the two users. Based on the two forms of trust, a so-called web of trust between community members is created which is often used in recommender systems helping users of e-commerce applications to get an idea about the trustworthiness of their mostly personally unknown cooperation partners. Unfortunately, however, these webs of trust are often too sparse to be helpful in practice since — at least in large online communities — a user has experience with only a very small fraction of the other community members. Thus, very often there will be no trust relation to an intended new partner of an e-commerce transaction at all (Kim et al., 2008). An alternative approach to the problem is advocated in this chapter. Based on collaborative filtering one can exploit the like-mindedness resp. similarity of individuals to infer trust to yet unknown parties which increases the trust relations in the web. For instance, if one knows that with respect to a specific property, two parties are trusted alike by a large number of different 11