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Dynamically computing reputation of recommender agents with learning capabilities
- Publication Year :
- 2008
-
Abstract
- 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 on real users show that our approach, implemented on the top of some well-known recommender systems, introduces significant improvements in terms of effectiveness.
Details
- Database :
- OAIster
- Notes :
- BADICA C, MANGIONI G, CARCHIOLO V, BURDESCU DD, Rosaci, D, Sarne', G, ROSACI D, SARNE' G
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1311397586
- Document Type :
- Electronic Resource