The amount of information on Internet is increasing very fast and, as a result, search becomes more and more a harder task. A common solution is to use authority-based search engines. However, for a community of people with similar interests, quality of results can be improved exploiting also implicit knowledge. We propose an agent-based recommendation system for supporting communities of people in searching the web by means of a popular search engine. Agents use data mining techniques in order to learn and discover users behaviors, and they interact to share knowledge about the users. We also present set of experimental results showing in terms of precision and recall how interaction increases the performance of the system.
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