On the reuse of past optimal queries
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
How people revisit web pages: empirical findings and implications for the design of history systems
International Journal of Human-Computer Studies - Special issue: World Wide Web usability
Information archiving with bookmarks: personal Web space construction and organization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Search histories for user support in user interfaces
Journal of the American Society for Information Science and Technology
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Web page revisitation revisited: implications of a long-term click-stream study of browser usage
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Information re-retrieval: repeat queries in Yahoo's logs
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
SearchBar: a search-centric web history for task resumption and information re-finding
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Large scale query log analysis of re-finding
Proceedings of the third ACM international conference on Web search and data mining
Multi-session re-search: in pursuit of repetition and diversification
Proceedings of the 21st ACM international conference on Information and knowledge management
FindAll: a local search engine for mobile phones
Proceedings of the 8th international conference on Emerging networking experiments and technologies
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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.