Selecting Service Providers Based on Reputation

Sandip Sen and Neelima Sajja

We consider the problem of user agents selecting service providers to process tasks. We assume that service providers are drawn from two populations: high and low-performing service providers with different averages but similar variance in performance. For selecting a service provider an user agent queries other user agents for their high/low rating of different service providers. We assume that there are a known percentage of "liar" users, who give false estimates of service providers. We develop a trust mechanism that determines the number of users to query given a target guarantee threshold likelihood of choosing high-performance service providers in the face of such "noisy" reputations. We evaluate the robustness of this reputation-based trusting mechanism over varying environmental parameters like percentage of liars, performance difference and variances for high and low-performing agents, learning rates, etc.

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