Agents for collaborative filtering

This paper describes a new generic agent-based framework for collaborative filtering. Usually, collaborative filtering tools use large collaborative document databases to model users' preferences. Nevertheless, we believe that collaborative filtering can be accomplished with decentralized systems in which user's preferences are learned from small individual databases. Such a distributed approach gives more flexibility in adding and removing users, and distributes computational effort naturally, economizing computing resources. In addition, it facilitates a continuous evolution of the users' preferences. At the same time, agents are commonly used to decentralize generic systems, which leads us to adopt a Multi-Agent System for representing the collaborative environment. In particular, we propose Personal Assistants to communicate with the user and to acquire users' models. Then, a Decision Agent uses such models to decide who must receive the incoming documents.