document: Modeling and predicting user behavior in sponsored search Implicit user feedback , including click-through and subsequent browsing behavior , is crucial for evaluating and improving the quality of results returned by search engines . Several recent studies ( 1 , 2 , 3 , 13 , 25 ) have used post-result browsing behavior including the sites visited , the number of clicks , and the dwell time on site in order to improve the ranking of search results . In this paper , we first study user behavior on sponsored search results ( i.e. , the advertisements displayed by search engines next to the organic results ) , and compare this behavior to that of organic results . Second , to exploit post-result user behavior for better ranking of sponsored results , we focus on identifying patterns in user behavior and predict expected on-site actions in future instances . In particular , we show how post-result behavior depends on various properties of the queries , advertisement , sites , and users , and build a classifier using properties such as these to predict certain aspects of the user behavior . Additionally , we develop a generative model to mimic trends in observed user activity using a mixture of pareto distributions . We conduct experiments based on billions of real navigation trails collected by a major search engine 's browser toolbar .
doc_bio_tags: O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O