document: CONQUER : a system for efficient context-aware query suggestions Many of today 's search engines provide autocompletion while the user is typing a query string . This type of dynamic query suggestion can help users to formulate queries that better represent their search intent during Web search interactions . In this paper , we demonstrate our query suggestion system called CONQUER , which allows to efficiently suggest queries for a given partial query and a number of available query context observations . The context-awareness allows for suggesting queries tailored to a given context , e.g. , the user location or the time of day . CONQUER uses a suggestion model that is based on the combined probabilities of sequential query patterns and context observations . For this , the weight of a context in a query suggestion can be adjusted online , for example , based on the learned user behavior or user profiles . We demonstrate the functionality of CONQUER based on 6 million queries from an AOL query log using the time of day and the country domain of the clicked URLs in the search result as context observations .
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