Agent-Based Service Selection

Abstract

This paper formulates the problem of service selection. It reformulates two traditional recommended approaches for service selection and proposes a new agent-based approach in which agents cooperate to evaluate service providers. In this approach, the agents rate each other, and autonomously decide how much to weigh each other's recommendations. The underlying algorithm with which the agents reason is developed in the context of a concept lattice, which enables finding relevant agents. Since large service selection datasets do not yet exist, for the purposes of evaluation, we reformulate the well-known product evaluations dataset MovieLens as a services dataset. We use it to compare the various approaches. Despite limiting the ow of information, the proposed approach compares well with the existing approaches in terms of some accuracy metrics defined within.

Keywords: service selection, agents, recommendation and rating systems