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Volumn , Issue , 2014, Pages 193-202

Modeling dwell time to predict click-level satisfaction

Author keywords

click satisfaction.; dwell time analysis; user behavior

Indexed keywords

DATA MINING; FORECASTING; INFORMATION RETRIEVAL; WEBSITES;

EID: 84904566992     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2556195.2556220     Document Type: Conference Paper
Times cited : (205)

References (46)
  • 1
    • 80052112523 scopus 로고    scopus 로고
    • Find it if you can: A game for modeling different types of web search success using interaction data
    • Ageev, M., Guo, Q., Lagun, D., and Agichtein, E. (2011). Find it if you can: a game for modeling different types of web search success using interaction data. Proc. SIGIR, 345-354.
    • (2011) Proc. SIGIR , pp. 345-354
    • Ageev, M.1    Guo, Q.2    Lagun, D.3    Agichtein, E.4
  • 2
    • 33750341480 scopus 로고    scopus 로고
    • Improving web search ranking by incorporating user behavior information
    • Agichtein, E., Brill, E., and Dumais, S. (2006). Improving web search ranking by incorporating user behavior information. Proc. SIGIR, 19-26.
    • (2006) Proc. SIGIR , pp. 19-26
    • Agichtein, E.1    Brill, E.2    Dumais, S.3
  • 3
    • 33750367864 scopus 로고    scopus 로고
    • Learning user interaction models for predicting web search result preferences
    • Agichtein, E., Brill, E., Dumais, S.T., and Ragno, R. (2006). Learning user interaction models for predicting web search result preferences. Proc. SIGIR, 3-10.
    • (2006) Proc. SIGIR , pp. 3-10
    • Agichtein, E.1    Brill, E.2    Dumais, S.T.3    Ragno, R.4
  • 6
    • 72449190651 scopus 로고    scopus 로고
    • Segment-level display time as implicit feedback: A comparison to eye tracking
    • Buscher, G., van Elst, L., and Dengel, A. (2009). Segment-level display time as implicit feedback: a comparison to eye tracking. Proc. SIGIR, 67-74.
    • (2009) Proc. SIGIR , pp. 67-74
    • Buscher, G.1    Van Elst, L.2    Dengel, A.3
  • 9
    • 0014599202 scopus 로고
    • Maximum likelihood estimation of the parameters of the gamma distribution and their bias
    • Choi, S.C. and Whette, R. (1969). Maximum likelihood estimation of the parameters of the gamma distribution and their bias. Technometrics, 11(4): 683-690.
    • (1969) Technometrics , vol.11 , Issue.4 , pp. 683-690
    • Choi, S.C.1    Whette, R.2
  • 11
    • 85061910249 scopus 로고    scopus 로고
    • A language modeling approach to predicting reading difficulty
    • Collins-Thompson, K. and Callan, J. (2004). A language modeling approach to predicting reading difficulty. Proc. HLT, 193-200.
    • (2004) Proc. HLT , pp. 193-200
    • Collins-Thompson, K.1    Callan, J.2
  • 13
    • 77956041878 scopus 로고    scopus 로고
    • Predicting searcher frustration
    • Feild, H., Allan, J., and Jones, R. (2010). Predicting searcher frustration. Proc. SIGIR, 34-41.
    • (2010) Proc. SIGIR , pp. 34-41
    • Feild, H.1    Allan, J.2    Jones, R.3
  • 15
    • 0035470889 scopus 로고    scopus 로고
    • Greedy function approximation: A gradient boosting machine
    • Friedman, J.H. (2001). Greedy function approximation: a gradient boosting machine. The Annals of Statistics, 29(5): 1189-1232.
    • (2001) The Annals of Statistics , vol.29 , Issue.5 , pp. 1189-1232
    • Friedman, J.H.1
  • 16
    • 84860857337 scopus 로고    scopus 로고
    • Beyond dwell time: Estimating document relevance from cursor movements and other post-click searcher behavior
    • Guo, Q. and Agichtein, E. (2012). Beyond dwell time: estimating document relevance from cursor movements and other post-click searcher behavior. Proc. WWW, 569-578.
    • (2012) Proc. WWW , pp. 569-578
    • Guo, Q.1    Agichtein, E.2
  • 17
    • 84866602673 scopus 로고    scopus 로고
    • Predicting query performance using query, result, and user interaction features
    • Guo, Q., White, R.W., Dumais, S.T., Wang, J., and Anderson, B. (2010). Predicting query performance using query, result, and user interaction features. Proc. RIAO, 198-201.
    • (2010) Proc. RIAO , pp. 198-201
    • Guo, Q.1    White, R.W.2    Dumais, S.T.3    Wang, J.4    Anderson, B.5
  • 19
    • 84866625880 scopus 로고    scopus 로고
    • A semi-supervised approach to modeling web search satisfaction
    • Hassan, A. (2012). A semi-supervised approach to modeling web search satisfaction. Proc. SIGIR, 275-284.
    • (2012) Proc. SIGIR , pp. 275-284
    • Hassan, A.1
  • 20
    • 77950930266 scopus 로고    scopus 로고
    • Beyond DCG: User behavior as a predictor of a successful search
    • Hassan, A., Jones, R., and Klinkner, K.L. (2010). Beyond DCG: user behavior as a predictor of a successful search. Proc. WSDM, 221-230.
    • (2010) Proc. WSDM , pp. 221-230
    • Hassan, A.1    Jones, R.2    Klinkner, K.L.3
  • 21
    • 84889597762 scopus 로고    scopus 로고
    • Beyond clicks: Query reformulation as a predictor of search satisfaction
    • Hassan, A., Shi, X., Craswell, N., and Ramsey, B. (2013). Beyond clicks: query reformulation as a predictor of search satisfaction. Proc. CIKM, 2019-2028.
    • (2013) Proc. CIKM , pp. 2019-2028
    • Hassan, A.1    Shi, X.2    Craswell, N.3    Ramsey, B.4
  • 22
    • 83055187787 scopus 로고    scopus 로고
    • A task level user satisfaction model and its application on improving relevance estimation
    • Hassan, A., Song, Y., and He, L. (2011). A task level user satisfaction model and its application on improving relevance estimation. Proc. CIKM, 125-134.
    • (2011) Proc. CIKM , pp. 125-134
    • Hassan, A.1    Song, Y.2    He, L.3
  • 23
    • 33747187264 scopus 로고    scopus 로고
    • Query performance prediction
    • DOI 10.1016/j.is.2005.11.003, PII S0306437905000955
    • He, B. and Ounis, I. (2006). Query performance prediction. Information System, 31(7): 585-594. (Pubitemid 44233987)
    • (2006) Information Systems , vol.31 , Issue.7 , pp. 585-594
    • He, B.1    Ounis, I.2
  • 25
    • 36448951157 scopus 로고    scopus 로고
    • How well does result relevance predict session satisfaction?
    • Huffman, S. and Hochster, M. (2007). How well does result relevance predict session satisfaction? Proc. SIGIR, 567-574.
    • (2007) Proc. SIGIR , pp. 567-574
    • Huffman, S.1    Hochster, M.2
  • 26
    • 0242456822 scopus 로고    scopus 로고
    • Optimizing search engines using clickthrough data
    • T. Joachims. (2002). Optimizing search engines using clickthrough data. Proc. SIGKDD, 132-142.
    • (2002) Proc. SIGKDD , pp. 132-142
    • Joachims, T.1
  • 27
    • 0031572682 scopus 로고    scopus 로고
    • A multivariate Kolmogorov-Smirnov test of goodness of fit
    • PII S0167715297000205
    • Justel, A., Peña, D., and Zamar, R. (1997). A multivariate Kolmogorov-Smirnov test of goodness of fit. Statistics & Probability Letters, 35(3): 251-259. (Pubitemid 127163069)
    • (1997) Statistics and Probability Letters , vol.35 , Issue.3 , pp. 251-259
    • Justel, A.1    Pena, D.2    Zamar, R.3
  • 28
    • 1542377552 scopus 로고    scopus 로고
    • Query type classification for web document retrieval
    • Kang, I.-H. and Kim, G. (2003). Query type classification for web document retrieval. Proc. SIGIR, 64-71.
    • (2003) Proc. SIGIR , pp. 64-71
    • Kang, I.-H.1    Kim, G.2
  • 29
    • 8644249427 scopus 로고    scopus 로고
    • Implicit feedback for inferring user preference: A bibliography
    • Kelly, D. and Teevan, J. (2003). Implicit feedback for inferring user preference: a bibliography. SIGIR Forum : 37(2).
    • (2003) SIGIR Forum , vol.37 , Issue.2
    • Kelly, D.1    Teevan, J.2
  • 30
    • 80053424504 scopus 로고    scopus 로고
    • Statistical estimation of word acquisition with application to readability prediction
    • Kidwell, P., Lebanon, G., and Collins-Thompson, K. (2009). Statistical estimation of word acquisition with application to readability prediction. Proc. EMNLP, 900-909.
    • (2009) Proc. EMNLP , pp. 900-909
    • Kidwell, P.1    Lebanon, G.2    Collins-Thompson, K.3
  • 31
    • 0034785488 scopus 로고    scopus 로고
    • Reading time, scrolling, and interaction: Exploring implicit sources of user preferences for relevance feedback
    • Kelly, D. and Belkin, N.J. (2001). Reading time, scrolling, and interaction: exploring implicit sources of user preferences for relevance feedback. Proc. SIGIR, 408-409
    • (2001) Proc. SIGIR , pp. 408-409
    • Kelly, D.1    Belkin, N.J.2
  • 32
    • 8644246883 scopus 로고    scopus 로고
    • Display time as implicit feedback: Understanding task effects
    • Kelly, D. and Belkin, N.J. (2004). Display time as implicit feedback: understanding task effects. Proc. SIGIR, 377-384.
    • (2004) Proc. SIGIR , pp. 377-384
    • Kelly, D.1    Belkin, N.J.2
  • 33
    • 84874247426 scopus 로고    scopus 로고
    • Playing by the rules: Mining query associations to predict search performance
    • Kim, Y., Hassan, A., White, R.W., and Wang, Y.-M. (2013). Playing by the rules: mining query associations to predict search performance. Proc. WSDM, 133-142.
    • (2013) Proc. WSDM , pp. 133-142
    • Kim, Y.1    Hassan, A.2    White, R.W.3    Wang, Y.-M.4
  • 34
    • 35348861901 scopus 로고    scopus 로고
    • Web projections: Learning from contextual sub graphs of the web
    • Leskovec, J., Dumais, S., and Horvitz, E. (2007). Web projections: learning from contextual sub graphs of the web. Proc. WWW, 471-480.
    • (2007) Proc. WWW , pp. 471-480
    • Leskovec, J.1    Dumais, S.2    Horvitz, E.3
  • 35
    • 77956052932 scopus 로고    scopus 로고
    • Understanding web browsing behaviors through Weibull analysis of dwell time
    • Liu C., White, R.W., and Dumais, S. (2010). Understanding web browsing behaviors through Weibull analysis of dwell time. Proc. SIGIR. 379-386.
    • (2010) Proc. SIGIR. , pp. 379-386
    • Liu, C.1    White, R.W.2    Dumais, S.3
  • 39
    • 67650085898 scopus 로고    scopus 로고
    • How does clickthrough data reflect retrieval quality?
    • Radlinski, F., Kurup, M. and Joachims, T. (2008). How does clickthrough data reflect retrieval quality? Proc. CIKM, 43-52.
    • (2008) Proc. CIKM , pp. 43-52
    • Radlinski, F.1    Kurup, M.2    Joachims, T.3
  • 41
    • 77956025820 scopus 로고    scopus 로고
    • Assessing the scenic route: Measuring the value of search trails in web logs
    • White, R.W. and Huang, J. (2010). Assessing the scenic route: measuring the value of search trails in web logs. Proc. Proc. SIGIR, 587-594.
    • (2010) Proc. Proc. SIGIR , pp. 587-594
    • White, R.W.1    Huang, J.2
  • 42
    • 34547626955 scopus 로고    scopus 로고
    • A study on the effects of personalization and task information on implicit feedback performance
    • White, R. W. and Kelly, D. (2006). A study on the effects of personalization and task information on implicit feedback performance. Proc. CIKM. 297-306.
    • (2006) Proc. CIKM , pp. 297-306
    • White, R.W.1    Kelly, D.2
  • 43
    • 57349177558 scopus 로고    scopus 로고
    • Deep classification in large-scale text hierarchies
    • Xue, G.-R., Xing, D., Yang, Q., and Yu, Y. (2008). Deep classification in large-scale text hierarchies. Proc. SIGIR, 619-626.
    • (2008) Proc. SIGIR , pp. 619-626
    • Xue, G.-R.1    Xing, D.2    Yang, Q.3    Yu, Y.4
  • 44
    • 84970893538 scopus 로고    scopus 로고
    • Silence is also evidence: Interpreting dwell time for recommendation from psychological perspective
    • Yin, P., Luo, P., Lee, W.-C., and Wang, M. (2013). Silence is also evidence: interpreting dwell time for recommendation from psychological perspective. Proc. KDD, 989-997.
    • (2013) Proc. KDD , pp. 989-997
    • Yin, P.1    Luo, P.2    Lee, W.-C.3    Wang, M.4
  • 45
    • 34547636493 scopus 로고    scopus 로고
    • Ranking robustness: A novel framework to predict query performance
    • Zhou, Y., and Croft, W.B. (2006). Ranking robustness: a novel framework to predict query performance. Proc. CIKM, 567-574.
    • (2006) Proc. CIKM , pp. 567-574
    • Zhou, Y.1    Croft, W.B.2
  • 46
    • 36448977901 scopus 로고    scopus 로고
    • Query performance prediction in web search environments
    • Zhou, Y. and Croft, W.B. (2007). Query performance prediction in web search environments. Proc. SIGIR, 543-550.
    • (2007) Proc. SIGIR , pp. 543-550
    • Zhou, Y.1    Croft, W.B.2


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