Personalized Search via Cluster Sensitive Ranking

Abstract

Web search has become amazingly powerful in its ability to discover and exploit nearly any kind of information that comprises the web. However, as powerful and large as current web search engine are , stilled limited in their ability to always deliver key services to their users especially when there is a considerable number of user with different search intentions and needs. In this paper, we study both analytically and empirically personalized search emphasizing their retrieval aspects. We also propose an analytical model for personalized search unifying four critical of the problem namely link structure, document content, user queries and user preference.