Let's browse: a collaborative browsing agent

THE STATE OF THE ART IN ADAPTIVE INFORMATION AGENTS

With the exponential growth of the Internet, information seekers are faced with the so-called problem of information overload. Adaptive Information Agents have been developed to alleviate this problem. The Main issues in the development of these agents are document representation, learning, and classification. Various paradigms have been explored for the development of adaptive information agents, and the performance of these agents differs in terms of computational efficiency, classification effectiveness, learning autonomy, exploration capability, and explanatory power. To develop a basic under-standing of the pros and cons of these paradigms, some representative information agents are examined. Such a review also serves to identify a general for the development of the next generation adaptive information agents.

IDENTIFYING TOPICS FOR WEB DOCUMENTS THROUGH FUZZY ASSOCIATION LEARNING

Due to the explosive growth of available information on the World Wide Web (WWW), users have suffered from the information overload. To alleviate this problem, there is a need for an intelligent tool to help the users screening and filtering for interesting and useful information. In this paper, a method of automatically identifying topics for Web documents via a classification technique is proposed. Topic identification can be applied as a filtering tool for recommender systems to prune down the number of documents to within some particular topics. We adopt the fuzzy association concept as a machine learning technique to classify the documents into some predefined categories or topics. Our approach is compared to the vector space model with the cosine coefficient using the data sets collected from three different Web portals: Yahoo!, Open Directory Project and Excite. The results show that our approach yields higher classification accuracy compared to the vector space model.