Dynamically Computing Reputation of Recommender Agents with Learning Capabilities

The importance of mutual monitoring in recommender systems based on learning agents derives from the consideration that a learning agent needs to interact with other agents in its environment in order to improve its individual performances. In this paper we present a novel framework, called EVA, that introduces a strategy to improve the performances of recommender agents based on a dynamic computation of the agent’s reputation. Some preliminary experiments show that our approach, implemented on the top of some well-known recommender systems, introduces significant improvements in terms of effectiveness.