A Personalized Recommendation System Based on Multi-agent

Recommendation systems are widely used to cope with the problem of information overload and, consequently, many recommendation methods have been developed for the present recommendation systems, such as content-based, collaborative filtering, Web mining-based and so on. But they are always lack of intelligence, self-adaptiveness and initiative. Aiming at these disadvantages, in this work, a personalized recommendation system (APRS) is presented with multi-agents based on Web intelligence though. This paper discusses the system structure of APRS at first. In addition, it discusses the functions of every component and the operating process in the system. This recommendation system allows multiple recommendation methods to cooperate with one another to present their best recommendations to the user, can meet the needs of multiple recommendation, and the Internet will appear some intelligent in the view of users.