measuring user preference changes in digital libraries
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.
user preference personalization stability web usage mining