Implicit: A Multi-agent Recommendation System for Web Search

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Aliaksandr Birukou, Enrico Blanzieri, Paolo Giorgini
Autonomous Agents and Multi-Agent Systems 24(1), pages 141-174

For people with non-ordinary interests, it is hard to search for information on the Internet because search engines are impersonalized and are more focused on “average” individuals with “standard” preferences. In order to improve web search for a community of people with similar but specific interests, we propose to use the implicit knowledge con- tained in the search behavior of groups of users. We developed a multi-agent recommendation system called Implicit, which supports web search for groups or communities of people. In Implicit, agents observe behavior of their users to learn about the “culture” of the community with specific interests. They facilitate sharing of knowledge about relevant links within the community by means of recommendations. The agents also recommend contacts, i.e., who in the community is the right person to ask for a specific topic. Experimental evaluation shows that Implicit improves the quality of the web search in terms of precision and recall.

journal or series
book Autonomous Agents and Multi-Agent Systems (J.AAMAS)