Bin Cao, Jian-Tao Sun, Evan Wei Xiang, Derek Hao Hu, Qiang Yang, and Zheng Chen
With the help of search engines, Web queries are becoming a major bridge between Web users and online services provided by search engines such as advertisement and Web
page search. Query classification (QC) is a task that aims to classify Web queries into topical categories. Since queries are usually short in length and ambiguous, the same query
may need to be classified to different categories according to different people's perspective. In this paper, we propose Personalized Query Classification (PQC) task and develop an algorithm based on user preferences learning. Users' preferences that are hidden in clickthrough logs are quite helpful for search engines to improve on their understanding of users' queries. We propose to connect query classification with preferences learning from clickthrough log for PQC. To tackle the sparseness problem in user preferences learning, we also propose a collaborative ranking model to leverage similar users' information. Experiments on a real world clickthrough log show that our proposed PQC algorithm can gain significant improvement compared with general QC.
Our method can be applied to a wide range of applications including personalized search and online advertising.
In The 18th ACM Conference on Information and Knowledge Management (CIKM 2009)
Publisher Association for Computing Machinery, Inc.
Copyright © 2007 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or firstname.lastname@example.org. The definitive version of this paper can be found at ACM’s Digital Library --http://www.acm.org/dl/.