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Modeling and predicting user behavior in sponsored search

Modeling and predicting user behavior in sponsored search,10.1145/1557019.1557135,Josh Attenberg,Sandeep Pandey,Torsten Suel

Modeling and predicting user behavior in sponsored search   (Citations: 6)
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Implicit user feedback, including click-through and subsequent brows- ing 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 advert isements 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 ac- tions in future instances. In particular, we show how post-r esult be- havior depends on various properties of the queries, advertisement, sites, and users, and build a classifier using properties suc h as these to predict certain aspects of the user behavior. Additional ly, we de- velop a generative model to mimic trends in observed user activ- ity using a mixture of pareto distributions. We conduct experiments based on billions of real navigation trails collected by a ma jor search engine's browser toolbar.
Conference: Knowledge Discovery and Data Mining - KDD , pp. 1067-1076, 2009
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