document: A community-aware search engine Current search technologies work in a `` one size fits all '' fashion . Therefore , the answer to a query is independent of specific user information need . In this paper we describe a novel ranking technique for personalized search servicesthat combines content-based and community-based evidences . The community-based information is used in order to provide context for queries andis influenced by the current interaction of the user with the service . Ouralgorithm is evaluated using data derived from an actual service available on the Web an online bookstore . We show that the quality of content-based ranking strategies can be improved by the use of communityinformation as another evidential source of relevance . In our experiments the improvements reach up to 48 % in terms of average precision .
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