Finding and reminding: file organization from the desktop
ACM SIGCHI Bulletin
Stuff I've seen: a system for personal information retrieval and re-use
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The perfect search engine is not enough: a study of orienteering behavior in directed search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
What do people recall about their documents?: implications for desktop search tools
Proceedings of the 12th international conference on Intelligent user interfaces
Building simulated queries for known-item topics: an analysis using six european languages
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Large scale analysis of web revisitation patterns
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Improved search engines and navigation preference in personal information management
ACM Transactions on Information Systems (TOIS)
Exploring memory in email refinding
ACM Transactions on Information Systems (TOIS)
Retrieval experiments using pseudo-desktop collections
Proceedings of the 18th ACM conference on Information and knowledge management
Ranking using multiple document types in desktop search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Towards realistic known-item topics for the ClueWeb
Proceedings of the 4th Information Interaction in Context Symposium
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Known-item search is the search for a specific document that is known to exist. This task is particularly important in Personal Information Management (PIM), where it is the most common search activity. A major obstacle to research in search technologies for PIM is the lack of publicly accessible test corpora. As a potential solution, pseudodesktop corpora and automatic query generation have been proposed. These approaches though do not take the cognitive processes into account that take place when a user formulates a re-finding query. The human memory is not perfect, and many factors influence a user's ability to recall information. In this work, we propose a model that accounts for these cognitive processes in the automatic query generation setting.