Identifying the Intent of a User Query Using Support Vector Machines
M. Mendoza, and J. Zamora. String Processing and Information Retrieval, (2009)
Abstract
In this paper we introduce a high-precision query classification method to identify the intent of a user query given that
it has been seen in the past based on informational, navigational, and transactional categorization. We propose using threevector representations of queries which, using support vector machines, allow past queries to be classified by user’s intents.The queries have been represented as vectors using two factors drawn from click-through data: the time users take to reviewthe documents they select and the popularity (quantity of preferences) of the selected documents. Experimental results showthat time is the factor that yields higher precision in classification. The experiments shown in this work illustrate thatthe proposed classifiers can effectively identify the intent of past queries with high-precision.
%0 Journal Article
%1 Mendoza09
%A Mendoza, Marcelo
%A Zamora, Juan
%D 2009
%J String Processing and Information Retrieval
%K WebSearch intentions
%P 131--142
%T Identifying the Intent of a User Query Using Support Vector Machines
%U http://dx.doi.org/10.1007/978-3-642-03784-9_13
%X In this paper we introduce a high-precision query classification method to identify the intent of a user query given that
it has been seen in the past based on informational, navigational, and transactional categorization. We propose using threevector representations of queries which, using support vector machines, allow past queries to be classified by user’s intents.The queries have been represented as vectors using two factors drawn from click-through data: the time users take to reviewthe documents they select and the popularity (quantity of preferences) of the selected documents. Experimental results showthat time is the factor that yields higher precision in classification. The experiments shown in this work illustrate thatthe proposed classifiers can effectively identify the intent of past queries with high-precision.
@article{Mendoza09,
abstract = {In this paper we introduce a high-precision query classification method to identify the intent of a user query given that
it has been seen in the past based on informational, navigational, and transactional categorization. We propose using threevector representations of queries which, using support vector machines, allow past queries to be classified by user’s intents.The queries have been represented as vectors using two factors drawn from click-through data: the time users take to reviewthe documents they select and the popularity (quantity of preferences) of the selected documents. Experimental results showthat time is the factor that yields higher precision in classification. The experiments shown in this work illustrate thatthe proposed classifiers can effectively identify the intent of past queries with high-precision.},
added-at = {2009-11-02T15:22:52.000+0100},
author = {Mendoza, Marcelo and Zamora, Juan},
biburl = {https://www.bibsonomy.org/bibtex/202a7877cb5dae392ff3f1d55fb26737c/mkroell},
description = {SpringerLink - Buchkapitel},
interhash = {04597d0711f9875f2a2610c5d0d61160},
intrahash = {02a7877cb5dae392ff3f1d55fb26737c},
journal = {String Processing and Information Retrieval},
keywords = {WebSearch intentions},
pages = {131--142},
timestamp = {2009-11-02T15:22:52.000+0100},
title = {Identifying the Intent of a User Query Using Support Vector Machines},
url = {http://dx.doi.org/10.1007/978-3-642-03784-9_13},
year = 2009
}