Qi Guo
ABSTRACT
An improved understanding of the relationship between search intent, result quality, and searcher behavior is crucial for improving the effectiveness of web search. While recent progress in user behavior mining has been largely focused on aggregate server-side click logs, we present a new class of search behavior models that also exploit fine-grained user interactions with the search results.
We show that mining these interactions, such as mouse movements and scrolling, can enable more effective detection of the user's search goals. Potential applications include automatic search evaluation, improving search ranking, result presentation, and search advertising. We describe extensive experimental evaluation over both controlled user studies, and logs of interaction data collected from hundreds of real users. The results show that our method is more effective than the current state-of-the-art techniques, both for detection of searcher goals, and for an important practical application of predicting ad clicks for a given search session.
- E. Agichtein, E. Brill, S. Dumais, and R. Ragno. Learning user interaction models for predicting web search result preferences. In Proc. of SIGIR, 2006. Google Scholar
Digital Library
- A. Ahmed and I. Traore. Detecting computer intrusions using behavioral biometrics. In Proc. of PST, 2005.Google Scholar
- R. Atterer, M. Wnuk, and A. Schmidt. Knowing the user's every move: user activity tracking for website usability evaluation and implicit interaction. In Proc. of WWW, 2006. Google ScholarDigital Library
- H. Becker, C. Meek, and D. Chickering. Modeling Contextual Factors of Click Rates. In Proc. of AAAI, 2007. Google ScholarDigital Library
- N. J. Belkin. User modeling in information retrieval. Tutorial at UM97, 1997.Google Scholar
- D. J. Brenes, D. Gayo-Avello, and K. Pérez-González. Survey and evaluation of query intent detection methods. In Proc. of WSCD workshop, pages 1--7, 2009. Google ScholarDigital Library
- A. Broder. A taxonomy of web search. SIGIR Forum, 2002. Google ScholarDigital Library
- A. Z. Broder, M. Ciaramita, M. Fontoura, E. Gabrilovich, V. Josifovski, D. Metzler, V. Murdock, and V. Plachouras. To swing or not to swing: Learning when (not) to advertise. In Proc. of CIKM, 2008. Google ScholarDigital Library
- G. Buscher, A. Dengel, and L. van Elst. Query expansion using gaze-based feedback on the subdocument level. In Proc. of SIGIR, 2008. Google ScholarDigital Library
- H. Cao, D. H. Hu, D. Shen, D. Jiang, J.-T. Sun, E. Chen, and Q. Yang. Context-aware query classification. In Proc. of SIGIR, 2009. Google ScholarDigital Library
- H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li. Context-aware query suggestion by mining click-through and session data. In Proc. of KDD, 2008. Google ScholarDigital Library
- M. Ciaramita, V. Murdock, and V. Plachouras. Online learning from click data for sponsored search. In Proc. of WWW, 2008. Google ScholarDigital Library
- N. Craswell and M. Szummer. Random walks on the click graph. In Proc. SIGIR, 2007. Google ScholarDigital Library
- E. Cutrell and Z. Guan. What are you looking for?: an eye-tracking study of information usage in web search. In Proc. of CHI, 2007. Google ScholarDigital Library
- H. K. Dai, L. Zhao, Z. Nie, J.-R. Wen, L. Wang, and Y. Li. Detecting online commercial intention (oci). In Proc. of WWW, 2006. Google ScholarDigital Library
- D. Downey, S. T. Dumais, and E. Horvitz. Models of searching and browsing: Languages, studies, and application. In Proc. of IJCAI, 2007. Google ScholarDigital Library
- S. Fox, K. Karnawat, M. Mydland, S. Dumais, and T. White. Evaluating implicit measures to improve web search. ACM Transactions on Information Systems, 23(2), 2005. Google ScholarDigital Library
- Q. Guo and E. Agichtein. Exploring mouse movements for inferring query intent. In Proc. SIGIR, 2008. Google ScholarDigital Library
- Q. Guo and E. Agichtein. Towards predicting web searcher gaze position from mouse movements. In Proc. CHI Extended Abstracts, 2010. Google ScholarDigital Library
- B. J. Jansen, D. L. Booth, and A. Spink. Determining the user intent of web search engine queries. In Proc. of WWW, 2007. 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), 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
- X. Li, Y.-Y. Wang, and A. Acero. Learning query intent from regularized click graphs. In SIGIR, pages 339--346, 2008. Google ScholarDigital Library
- J. G. Phillips and T. J. Triggs. Characteristics of cursor trajectories controlled by the computer mouse. Ergonomics, 2001.Google Scholar
- B. Piwowarski, G. Dupret, and R. Jones. Mining user web search activity with layered bayesian networks or how to capture a click in its context. In Proc. of WSDM, 2009. Google ScholarDigital Library
- F. Radlinski and T. Joachims. Query chains: learning to rank from implicit feedback. In Proc. of KDD, 2005. Google ScholarDigital Library
- M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: estimating the click-through rate for new ads. In Proc. of WWW, 2007. Google ScholarDigital Library
- K. Rodden, X. Fu, A. Aula, and I. Spiro. Eye-mouse coordination patterns on web search results pages. In Proc. of CHI, 2008. Google ScholarDigital Library
- D. E. Rose and D. Levinson. Understanding user goals in web search. In Proc. of WWW, 2004. Google ScholarDigital Library
- D. Sculley, R. G. Malkin, S. Basu, and R. J. Bayardo. Predicting bounce rates in sponsored search advertisements. In Proc. of KDD, 2009. Google ScholarDigital Library
- J. Teevan, S. T. Dumais, and D. J. Liebling. To personalize or not to personalize: modeling queries with variation in user intent. In Proc. of SIGIR, 2008. Google ScholarDigital Library
- K. Wang, N. Gloy, and X. Li. Inferring search behaviors using partially observable Markov (POM) model. In Proc. of WSDM, 2010. Google ScholarDigital Library
- R. W. White and S. M. Drucker. Investigating behavioral variability in web search. In Proc. of WWW, 2007. Google ScholarDigital Library
- R. W. White and R.A. Roth. Exploratory Search: Beyond the Query-Response Paradigm. Morgan & Claypool Synthesis Lectures on Information Concepts, Retrieval, and Services, 2009. Google ScholarDigital Library
Index Terms
- Ready to buy or just browsing?: detecting web searcher goals from interaction data
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