Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Investigating behavioral variability in web search
Proceedings of the 16th international conference on World Wide Web
Mining the search trails of surfing crowds: identifying relevant websites from user activity
Proceedings of the 17th international conference on World Wide Web
PSkip: estimating relevance ranking quality from web search clickthrough data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Smoothing clickthrough data for web search ranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Studying trailfinding algorithms for enhanced web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Assessing the scenic route: measuring the value of search trails in web logs
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 21st international conference on World Wide Web
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With increasing popularity of browser toolbars, the challenge of employing user behavior data stored in their logs rises in its importance. The analysis of post-click search trails was shown to provide important knowledge about user experience, helpful for improving existing search systems. However, the utility of different trail properties for improving existing ranking models is still underexplored. We conduct a large-scale study and evaluation of a rich set of search trail features in realistic settings and conclude that a deeper investigation of a users experience far beyond her click on the result page has the potential to improve the existing ranking models.