Search

Web information may currently be acquired by activating search engines. However, our daily experience is not only that web pages are often either redundant or missing but also that there is a mismatch between information needs and the web's responses. If we wish to satisfy more complex requests, we need to extract part of the information and transform it into new interactive knowledge. This transformation may either be performed by hand or automatically. In this article we describe an experimental agent-based framework skilled to help the user both in managing achieved information and in personalizing web searching activity. The first process is supported by a query-formulation facility and by a friendly structured representation of the searching results. On the other hand, the system provides a proactive support to the searching on the web by suggesting pages, which are selected according to the user's behavior shown in his navigation activity. A basic role is played by an extension of a classical fuzzy-clustering algorithm that provides a prototype-based representation of the knowledge extracted from the web. These prototypes lead both the proactive suggestion of new pages, mined through web spidering, and the structured representation of the searching results. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1101–1122, 2007. [ABSTRACT FROM AUTHOR]

The explosive growth of the Web and the consequent exigency of the Web personalization domain have gained a key position in the direction of customization of the Web information to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. This work presents an agent-based framework designed to help a user in achieving personalized navigation, by recommending related documents according to the user's responses in similar-pages searching mode. Our agent-based approach is grounded in the integration of different techniques and methodologies into a unique platform featuring user profiling, fuzzy multisets, proximity-oriented fuzzy clustering, and knowledge-based discovery technologies. Each of these methodologies serves to solve one facet of the general problem (discovering documents relevant to the user by searching the Web) and is treated by specialized agents that ultimately achieve the final functionality through cooperation and task distribution. [ABSTRACT FROM AUTHOR]