ABSTRACT
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.
- E. Adar, J. Teevan, and S. T. Dumais. Large scale analysis of web revisitation patterns. In Proc. of SIGCHI 2008. Google ScholarDigital Library
- E. Agichtein, E. Brill, and S. Dumais. Improving web search ranking by incorporating user behavior information. In Proc. of SIGIR 2006. Google ScholarDigital Library
- E. Agichtein and Z. Zheng. Identifying "best bet" web search results by mining past user behavior. In Proc. of KDD 2006. Google ScholarDigital Library
- R. Baeza-Yates and C. Castillo. Crawling the infinite web: five levels are enough. In 3rd Workshop on Algorithms and Models for the Web-Graph, 2004.Google ScholarCross Ref
- M. Bilenko and R.W. White. Mining the search trails of surfing crowds: Identifying relevant websites from user activity. In Proc. of WWW 2008. Google ScholarDigital Library
- S. Brin and L. Page. The anatomy of a large-scale hypertextual search engine. In Proc. of WWW 1998. Google ScholarDigital Library
- A. Broder. A taxonomy of web search. SIGIR Forum, 36, 2002. Google ScholarDigital Library
- A. Broder, P. Ciccolo, M. Fontoura, E. Gabrilovich, V. Josifovski, and L. Riedel. Search advertising using web relevance feedback. In Proc. of CIKM 2008. Google ScholarDigital Library
- A. Z. Broder. Computational advertising and recommender systems. In RecSys '08: Proc. of the 2008 ACM Conf. on Recommender systems, 2008. Google ScholarDigital Library
- E. H. Chi, P. Pirolli, K. Chen, and J. Pitkow. Using information scent to model user information needs and actions and the web. In Proc. of SIGCHI 2001. Google ScholarDigital Library
- A. Clauset, C. R. Shalizi, and M. E. J. Newman. Power-law distributions in empirical data. ArXiv Technical Report, 2007.Google Scholar
- A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Royal statistical Society B, 39:1--38, 1977.Google Scholar
- S. Fox, K. Karnawat, M. Mydland, S. Dumais, and T.White. Evaluating implicit measures to improve web search. ACM Trans. Inf. Syst., 23(2):147--168, 2005. Google ScholarDigital Library
- B. H. Hager, M. A. Richards, P. T. R, B. A. Huberman, P. L. T. Pirolli, J. E. Pitkow, and R. M. Lukose. Strong regularities in world wide web surfing. Science, 280:95--97, 1998.Google ScholarCross Ref
- T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting clickthrough data as implicit feedback. In Proc. of SIGIR 2005. Google ScholarDigital Library
- T. Joachims, L. Granka, B. Pan, H. Hembrooke, F. Radlinski, and G. Gay. Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search. ACM Trans. Inf. Syst., 25(2):7, 2007. Google ScholarDigital Library
- U. Lee, Z. Liu, and J. Cho. Automatic identification of user goals in web search. In Proc. of WWW 2005. Google ScholarDigital Library
- Y. Liu, B. Gao, T.-Y. Liu, Y. Zhang, Z. Ma, S. He, and H. Li. Browserank: letting web users vote for page importance. In Proc of SIGIR 2008. Google ScholarDigital Library
- M. R. Meiss, F. Menczer, S. Fortunato, A. Flammini, and A. Vespignani. Ranking web sites with real user traffic. In Proc. of WSDM 2008. Google ScholarDigital Library
- F. Radlinski, A. Broder, P. Ciccolo, E. Gabrilovich, V. Josifovski, and L. Riedel. Optimizing relevance and revenue in ad search: a query substitution approach. In Proc. of SIGIR 2008. Google ScholarDigital Library
- F. Radlinski and T. Joachims. Query chains: learning to rank from implicit feedback. In Proc. of SIGKDD 2005. Google ScholarDigital Library
- F. Radlinski, M. Kurup, and T. Joachims. How does clickthrough data reflect retrieval quality? In Proc. of CIKM 2008. Google ScholarDigital Library
- J. Teevan, C. Alvarado, M. S. Ackerman, and D. R. Karger. The perfect search engine is not enough: a study of orienteering behavior in directed search. In Proc. of SIGCHI 2004. Google ScholarDigital Library
- R. W. White, M. Bilenko, and S. Cucerzan. Studying the use of popular destinations to enhance web search interaction. In Proc. of SIGIR 2007. Google ScholarDigital Library
- R. W. White and S. M. Drucker. Investigating behavioral variability in web search. In Proc of WWW 2007. Google ScholarDigital Library
Index Terms
- Modeling and predicting user behavior in sponsored search
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