Mining broad hidden query aspects from user search sessions

Abstract

An optimization-based framework is utilized to extract broad query aspects from query reformulations performed by users in historical user session logs. Objective functions are optimized to yield query aspects. At run-time, the best broad but unspecified query aspects relevant to any user query are presented along with the results of the run time query.

What is claimed is: 
     
         1 . A computer-implemented method for providing search results, comprising:
 analyzing search logs for (i) a first query comprising a first search term, followed by (ii) a second query comprising the first search term and a qualifier not initially specified in the first query;   determining k aspects of the qualifier;   receiving an original query at run time; and   providing in response to the original query at least one of the k aspects along with results of the original query.   
     
     
         2 . The method of  claim 1 , wherein determining k aspects of the qualifier comprises clustering the first search term and qualifier. 
     
     
         3 . The method of  claim 2 , wherein determining k aspects of the qualifier further comprises selecting from clusters resulting from the clustering. 
     
     
         4 . The method of  claim 2 , wherein determining k aspects of the qualifier further comprises an inter cluster move of an aspect from a first cluster to a second cluster. 
     
     
         5 . The method of  claim 1 , wherein determining k aspects of the qualifier comprises applying modified star clustering. 
     
     
         6 . The method of  claim 1 , wherein determining k aspects of the qualifier comprises applying k means clustering. 
     
     
         7 . A computerized searching system configured to:
 analyze search logs for (i) a first query comprising a first search term, followed by (ii) a second query comprising the first search term and a qualifier not initially specified in the first query;   determine k aspects of the qualifier;   receive an original query at run time; and   providing in response to the original query at least one of the k aspects along with results of the original query.   
     
     
         8 . The system of  claim 7 , wherein determining k aspects of the qualifier comprises clustering the first search term and qualifier. 
     
     
         9 . The system of  claim 8 , wherein determining k aspects of the qualifier further comprises selecting from clusters resulting from the clustering. 
     
     
         10 . The system of  claim 8 , wherein determining k aspects of the qualifier further comprises an inter cluster move of an aspect from a first cluster to a second cluster. 
     
     
         11 . The system of  claim 7 , wherein determining k aspects of the qualifier comprises applying modified star clustering. 
     
     
         12 . The system of  claim 7 , wherein determining k aspects of the qualifier comprises applying k means clustering. 
     
     
         13 . The system of  claim 7 , wherein the original query comprises the first search term. 
     
     
         14 . At least one computer readable storage medium having computer program instructions stored thereon that are arranged to perform the following operations:
 analyzing search logs for (i) a first query comprising a first search term, followed by (ii) a second query comprising the first search term and a qualifier not initially specified in the first query;   determining k aspects of the qualifier;   receiving an original query at run time; and   providing in response to the original query at least one of the k aspects along with results of the original query.   
     
     
         15 . The computer readable storage medium of  claim 14 , wherein determining k aspects of the qualifier comprises clustering the first search term and qualifier. 
     
     
         16 . The computer readable storage medium of  claim 15 , wherein determining k aspects of the qualifier further comprises selecting from clusters resulting from the clustering. 
     
     
         17 . The computer readable storage medium of  claim 15 , wherein determining k aspects of the qualifier further comprises an inter cluster move of an aspect from a first cluster to a second cluster. 
     
     
         18 . The computer readable storage medium of  claim 14 , wherein determining k aspects of the qualifier comprises applying modified star clustering. 
     
     
         19 . The computer readable storage medium of  claim 14 , wherein determining k aspects of the qualifier comprises applying k means clustering. 
     
     
         20 . The computer readable storage medium of  claim 14 , wherein the original query comprises the first search term.