CiteSeer: an automatic citation indexing system
Proceedings of the third ACM conference on Digital libraries
Learning user interest dynamics with a three-descriptor representation
Journal of the American Society for Information Science and Technology
Modeling user interest shift using a Bayesian approach
Journal of the American Society for Information Science and Technology
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Implicit user modeling for personalized search
Proceedings of the 14th ACM international conference on Information and knowledge management
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Much research has been conducted using web access logs to study implicit user feedback and infer user preferences from clickstreams. However, little research measures the changes of user preferences of ranking documents over time. We present a study that measures the changes of user preferences based on an analysis of access logs of a large scale digital library over one year. A metric based on the accuracy of predicting future user actions is proposed. The results show that although user preferences change over time, the majority of user actions should be predictable from previous browsing behavior in the digital library.