The problem of selecting correct counterparts to interact with is of particular relevance in open and dynamic environments. This problem increases when third parties may vary their behaviour at will. In this paper we examine the problem of service provider selection using trust and reputation techniques. Most approaches to service provider selection are based on the client's proper experiences about particular services from particular providers. A problem arises when no previous experience is available. To solve this problem, previous approaches have proposed that clients obtain the required reputation information from their acquaintances. In contrast, our work advocates an experience-based approach for service provider selection, in which clients use trust and reputation mechanisms to infer expectations of future providers' behaviour from past experiences in similar situations. We present some experimental results that support our proposal.

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In this work, we study the potential problems emanating from using numerical ratings in social networks to rank entities regarding their reputation. In particular, we empirically demonstrate how reputation rankings as collected and managed by current systems are likely to be skewed due to subjectivity problems associated with the use of numerical ratings to encapsulate preferences. With the aim of overcoming these problems, we put forward an approach in which users are asked for their opinions about entities in a comparative fashion. In order to select the most appropriate users to be queried, we take advantage of the social structure derived from the interactions among users and entities following a principle of heterogeneity. Finally, we evaluate the proposed approach in the domain of movie ratings by using real datasets collected from different web sites.

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