document: Automatic identification of user interest for personalized search One hundred users , one hundred needs . As more and more topics are being discussed on the web and our vocabulary remains relatively stable , it is increasingly difficult to let the search engine know what we want . Coping with ambiguous queries has long been an important part of the research on Information Retrieval , but still remains a challenging task . Personalized search has recently got significant attention in addressing this challenge in the web search community , based on the premise that a user 's general preference may help the search engine disambiguate the true intention of a query . However , studies have shown that users are reluctant to provide any explicit input on their personal preference . In this paper , we study how a search engine can learn a user 's preference automatically based on her past click history and how it can use the user preference to personalize search results . Our experiments show that users ' preferences can be learned accurately even from little click-history data and personalized search based on user preference yields significant improvements over the best existing ranking mechanism in the literature .
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