Utilizing re-finding for personalized information retrieval
Sarah K. Tyler, Jian Wang, Yi Zhang · 2010
Individuals often use search engines to return to web pages they have previously visited. This behaviour, called re-finding, 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 re-finding for personalized search. We compared three machine learning algorithms (decision trees, Bayesian multinomial regression and support vector machines) to identify re-findings. 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.