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Through-the-looking glass: utilizing rich post-search trail statistics for web search

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.