The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Focused Crawling Using Context Graphs
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
WWW '03 Proceedings of the 12th international conference on World Wide Web
A Unified Probabilistic Framework for Web Page Scoring Systems
IEEE Transactions on Knowledge and Data Engineering
Communications of the ACM - The disappearing computer
A learning algorithm for web page scoring systems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Journal of the American Society for Information Science and Technology
Surveying the identification of communities
International Journal of Web Based Communities
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The conception of appropriate models of information retrieval is a crucial step for the actual growth and development of the web. In addition to traditional information retrieval techniques, nowadays search engines have adopted methods for taking into account the social network deriving from the pattern of interconnections. Using different arguments, it has been shown that this is very effective in practice. In this paper we subscribe that point of view, but state also that social networks based ranking schemes operating on the whole web, like Google's pagerank, prevent small communities to become visible on the Web, regardless of the quality of the information. Hence, we strongly advocate the development of new cognitive models of the whole process of information retrieval which are capable of facing this problem. We give a picture of distributed architectures which incorporates topic-based intelligent agents and personal agents for capturing the user's profile.