Utilizing re-finding for personalized information retrieval

Individuals often use search engines to return to web pages they have previously visited. This behaviour, called refinding, accounts for about 38% of all queries. While researchers have shown how re-finding differs from traditionally studied new-findings, research on how to predict and utilize re-finding is limited. In this paper we explore refinding for personalized search. We compared three machine learning algorithms (decision trees, Bayesian multinomial regression and support vector machines) to identify refindings. We then propose several re-ranking methods to utilize the prediction, including promoting predicted re-finding URLs and combining re-finding prediction with relevance estimation. The experimental results demonstrate that using re-finding predictions can improve retrieval performance for personalized search. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: General Terms Algorithms, Design, Experimentation Keywords Re-finding, Query Log Analys...

• Type: Journal
• Year: 2010
• Where: CIKM
• Authors: Sarah K. Tyler, Jian Wang, Yi Zhang 0001
• Keywords: Bayesian Multinomial Regression, CIKM 2010, Information Technology, Personalized, Support Vector Machines