MINING BROAD HIDDEN QUERY ASPECTS FROM USER SEARCH SESSIONS
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.
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.