Consensus-based evaluation framework for cooperative information retrieval systems

Multi-agent systems have been attacking the challenges of distributed information retrieval. In this paper, we propose a consensus method-based framework to evaluate the performance of cooperative information retrieval tasks of the agents. Two well-known measurements, precision and recall, are extended to handle consensual closeness (i.e., local and global consensus) between the retrieved results. We show in a motivating example that the proposed criteria are prone to solve the problem of rigidity of classical precision and recall. More importantly, the retrieved results can be ranked with respect to the consensual score.