RECOMMENDING SERVICES IN A DIFFERNTIATED TRUST-BASED DECENTRALIZED USER MODELING SYSTEM

Trust and reputation mechanisms are often used in peer-to-peer networks, multi-agent systems and online communities for trust-based interactions among the users. Trust values are used to differentiate among members of the community as well as to recommend service providers. Although different users have different needs and expectations in different aspects of the service providers, traditional trust-based models do not use trust values on neighbors for judging different aspects of service providers. In this thesis, I use multi-faceted trust models for users connected in a network who are looking for suitable service providers according to their preferences. Each user has two sets of trust values: i) trust in different aspects of the quality of service providers, ii) trust in recommendations provided for these aspects. These trust models are used in a decentralized user modeling system where agents (representing users) have different preference weights in different criteria of service providers. My approach helps agents by recommending the best possible service provider for each agent according to its preferences. The approach is evaluated by conducting simulation on both small and large social networks. The results of the experiments illustrate that agents find better matches or more suitable service providers for themselves using my trust-based recommender system without the help of any central server. To the best of my knowledge this is the first system that uses multi-faceted trust values both in the qualities of service-providers and in other users’ ability to evaluate these qualities of service providers in a decentralized user modeling system