document: On ranking techniques for desktop search This paper addresses the desktop search problem by considering varioustechniques for ranking results of a search query over thefile system . First , basic ranking techniques , which are based ona single file feature ( e.g. , file name , file content , access date , etc. ) are considered . Next , two learning-based ranking schemes are presented , and are shown to be significantly more effective than the basic ranking methods . Finally , a novel ranking technique , based on query selectiveness is considered , for use during the cold-start period of the system . This method isalso shown to be empirically effective , even though it does notinvolve any learning .
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