Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Display time as implicit feedback: understanding task effects
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
A study of factors affecting the utility of implicit relevance feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
NLTK: the Natural Language Toolkit
ETMTNLP '02 Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics - Volume 1
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A study on the effects of personalization and task information on implicit feedback performance
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A faceted approach to conceptualizing tasks in information seeking
Information Processing and Management: an International Journal
Expected reciprocal rank for graded relevance
Proceedings of the 18th ACM conference on Information and knowledge management
ACM Transactions on Computer-Human Interaction (TOCHI)
Personalizing information retrieval for multi-session tasks: the roles of task stage and task type
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Helping identify when users find useful documents: examination of query reformulation intervals
Proceedings of the third symposium on Information interaction in context
Utilizing query change for session search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Personalization of search results offers the potential for significant improvement in information retrieval performance. User interactions with the system and documents during information-seeking sessions provide a wealth of information about user preferences and their task goals. In this paper, we propose methods for analyzing and modeling user search behavior in search sessions to predict document usefulness and then using information to personalize search results. We generate prediction models of document usefulness from behavior data collected in a controlled lab experiment with 32 participants, each completing uncontrolled searching for 4 tasks in the Web. The generated models are then tested with another data set of user search sessions in radically different search tasks and constrains. The documents predicted useful and not useful by the models are used to modify the queries in each search session using a standard relevance feedback technique. The results show that application of the models led to consistently improved performance over a baseline that did not take account of user interaction information. These findings have implications for designing systems for personalized search and improving user search experience.