A MultiAgent System for Personalized Press Reviews

The continuous growth of Internet information sources, together with the corresponding volume of daily-updated contents, makes the problem of nding news and articles a challenging task. This paper presents a multiagent system aimed at creating personalized press reviews from online newspapers by progressively ltering information that ows from sources to the end user, so that only relevant articles are retained. First, newspaper articles are classi ed according to a high-level taxonomy that does not depend on a speci c user. Then, a personalized classi cation is performed according to user needs and preferences. Moreover, an optional feedback provided by the user is exploited to improve the system precision and recall. The system is built upon a generic multiagent architecture that supports the implementation of personalized, adaptive and cooperative multiagent systems aimed at retrieving, ltering and reorganizing information in a web-based environment. Experimental results show that the proposed approach is e ective in the given application task.