PQC

Bin Cao, Jian-Tao Sun, Evan Wei Xiang, Derek Hao Hu, Qiang Yang, Zheng Chen
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
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 perspectives. In this paper, we propose the Personalized Query Classification (PQC) task and develop an algorithm based on user preference learning as a solution. Users' preferences that are hidden in clickthrough logs are quite helpful for search
more » ... ines to improve their understandings of users' queries. We propose to connect query classification with users' preference learning from clickthrough logs for PQC. To tackle the sparseness problem in clickthrough logs, we propose a collaborative ranking model to leverage similar users' information. Experiments on a real world clickthrough log data show that our proposed PQC algorithm can gain significant improvement compared with general QC as well as natural baselines. Our method can be applied to a wide range of applications including personalized search and online advertising.
doi:10.1145/1645953.1646108 dblp:conf/cikm/CaoSXHYC09 fatcat:2d4jgxxphfbb3e4p6hbpthw2wu