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Behavioral dynamics on the web: Learning, modeling, and prediction

Authors:

Krysta M. Svore,

Susan T. Dumais,

Milad Shokouhi,

Eric HorvitzAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 31, Issue 3

Article No.: 16, Pages 1 - 37

https://doi.org/10.1145/2493175.2493181

Published: 05 August 2013 Publication History

Abstract

The queries people issue to a search engine and the results clicked following a query change over time. For example, after the earthquake in Japan in March 2011, the query japan spiked in popularity and people issuing the query were more likely to click government-related results than they would prior to the earthquake. We explore the modeling and prediction of such temporal patterns in Web search behavior. We develop a temporal modeling framework adapted from physics and signal processing and harness it to predict temporal patterns in search behavior using smoothing, trends, periodicities, and surprises. Using current and past behavioral data, we develop a learning procedure that can be used to construct models of users' Web search activities. We also develop a novel methodology that learns to select the best prediction model from a family of predictive models for a given query or a class of queries. Experimental results indicate that the predictive models significantly outperform baseline models that weight historical evidence the same for all queries. We present two applications where new methods introduced for the temporal modeling of user behavior significantly improve upon the state of the art. Finally, we discuss opportunities for using models of temporal dynamics to enhance other areas of Web search and information retrieval.

References

[1]

Adar, E., Weld, D. S., Bershad, B. N., and Gribble, S. D. 2007. Why we search: Visualizing and predicting user behavior. In Proceedings of the International World Wide Web Conference (WWW).

Digital Library

[2]

Agichtein, E., Brill, E., and Dumais, S. T. 2006. Improving Web search ranking by incorporating user behavior information. In Proceedings of the Annual Special Interest Group on Information Retrieval Conference (SIGIR).

Digital Library

[3]

Bar-Yossef, Z. and Kraus, N. 2011. Context-sensitive query auto-completion. In Proceedings of the International World Wide Web Conference (WWW). 107--116.

Digital Library

[4]

Beitzel, S. M., Jensen, E. C., Chowdhury, A., Grossman, D., and Frieder, O. 2004. Hourly analysis of a very large topically categorized Web query log. In Proceedings of the Annual Special Interest Group on Information Retrieval Conference (SIGIR).

Digital Library

[5]

Bennett, P. N., Svore, K., and Dumais, S. T. 2010. Classification-enhanced ranking. In Proceedings of the International World Wide Web Conference (WWW).

Digital Library

[6]

Bickel, S., Haider, P., and Scheffer, T. 2005. Learning to complete sentences. In Proceedings of the European Conference on Machine Learning (ECML). 497--504.

Digital Library

[7]

Blei, D. M., Ng, A. Y., and Jordan, M. I. 2003. Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993--1022.

[8]

Bogert, B., Healy, M., and Tukey, J. 1967. Cepstrum pitch determination. J. Acoust. Soc. Amer. 41, 2, 293--309.

[9]

Burges, C. J. C. 2010. From RankNet to LambdaRank to LambdaMART: An overview. Tech. rep. MSR-TR-2010-82, Microsoft Research.

[10]

Chaudhuri, S. and Kaushik, R. 2009. Extending autocompletion to tolerate errors. In Proceedings of the International Conference on Management of Data (SIGMOD). 707--718.

Digital Library

[11]

Chien, S. and Immorlica, N. 2005. Semantic similarity between search engine queries using temporal correlation. In Proceedings of the International World Wide Web Conference (WWW).

Digital Library

[12]

Childers, D., Skinner, D., and Kemerait, R. 1977. The cepstrum: A guide to processing. Proc. IEEE 65, 10, 1428--1443.

[13]

Dai, N., Shokouhi, M., and Davison, B. D. 2011. Learning to rank for freshness and relevance. In Proceedings of the Annual Special Interest Group on Information Retrieval Conference (SIGIR).

Digital Library

[14]

Dakka, W., Gravano, L., and Ipeirotis, P. G. 2008. Answering general time sensitive queries. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM).

Digital Library

[15]

Darragh, J. J., Witten, I. H., and James, M. L. 1990. The reactive keyboard: A predictive typing aid. Computer 23, 41--49.

Digital Library

[16]

Diaz, F. 2009. Integration of news content into Web results. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM).

Digital Library

[17]

Dong, A., Chang, Y., Zheng, Z., Mishne, G., Bai, J., Zhang, R., Buchner, K., Liao, C., and Diaz, F. 2010a. Towards recency ranking in Web search. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM).

Digital Library

[18]

Dong, A., Zhang, R., Kolari, P., Bai, J., Diaz, F., Chang, Y., Zheng, Z., and Zha, H. 2010b. Time is of the essence: Improving recency ranking using Twitter data. In Proceedings of the International World Wide Web Conference (WWW).

Digital Library

[19]

Dunn, P. F. 2005. Measurement and Data Analysis for Engineering and Science. McGraw-Hill, New York, NY.

[20]

Durbin, J. and Koopman, S. 2008. Time Series Analysis by State Space Methods. Oxford University Press, Oxford, UK.

[21]

Efron, M. 2010. Linear time series models for term weighting in information retrieval. J. Amer. Soc. Inf. Sci. Technol. 6, 7.

Digital Library

[22]

Efron, M. and Golovchinksy, G. 2011. Estimation methods for ranking recent information. In Proceedings of the Annual Special Interest Group on Information Retrieval Conference (SIGIR).

Digital Library

[23]

Elsas, J. L. and Dumais, S. T. 2010. Leveraging temporal dynamics of document content in relevance ranking. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM).

Digital Library

[24]

Fan, J., Wu, H., Li, G., and Zhou, L. 2010. Suggesting topic-based query terms as you type. In Proceedings of the International Asia-Pacific Web Conference (APWeb). 61--67.

Digital Library

[25]

Ginsberg, J., Mohebbi, M., Patel, R., Brammer, L., Smolinski, M., and Brilliant, L. 2009. Detecting influenza epidemics using search engine query data. Nature 457, 7232, 1012--4.

[26]

Grabski, K. and Scheffer, T. 2004. Sentence completion. In Proceedings of the Annaul Special Interest Group on Information Retrieval Conference (SIGIR). 433--439.

Digital Library

[27]

Holt, C. C. 2004. Forecasting seasonals and trends by exponentially weighted moving averages. Int. J. Forecas. 20, 1, 5--10.

[28]

Hyndman, R., Koehler, A., Ord, J., and Snyder, R. 2008. Forecasting with Exponential Smoothing (The State Space Approach). Springer, Berlin.

[29]

Ji, S., Li, G., Li, C., and Feng, J. 2009. Efficient interactive fuzzy keyword search. In Proceedings of the International World Wide Web Conference (WWW). 371--380.

Digital Library

[30]

Jones, R. and Diaz, F. 2007. Temporal profiles of queries. ACM Trans. Inform. Syst. 25, 3, 14.

Digital Library

[31]

Kleinberg, J. 2002. Bursty and hierarchical structure in streams. In Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (KDD).

Digital Library

[32]

Kleinberg, J. 2006. Temporal dynamics of on-line information systems. In Data Stream Management: Processing High-Speed Data Streams, Springer, Berlin.

[33]

König, A. C., Gamon, M., and Wu, Q. 2009. Click-through prediction for news queries. In Proceedings of the Annual Special Interest Group on Information Retrieval Conference (SIGIR).

Digital Library

[34]

Koren, Y. 2009. Collaborative filtering with temporal dynamics. In Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (KDD).

Digital Library

[35]

Kulkarni, A., Teevan, J., Svore, K. M., and Dumais, S. T. 2011. Understanding temporal query dynamics. In Proceedings of the 4th International Conference on Web Search and Data Mining (WSDM'11).

Digital Library

[36]

Lau, T. and Horvitz, E. 1998. Patterns of search: Analyzing and modeling Web query refinement. In Proceedings of the 7th International Conference on User Modeling.

Digital Library

[37]

Li, X. and Croft, W. B. 2003. Time-based language models. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM).

Digital Library

[38]

Metzler, D., Jones, R., Peng, F., and Zhang, R. 2009. Improving search relevance for implicitly temporal queries. In Proceedings of the Annual Special Interest Group on Information Retrieval Conference (SIGIR).

Digital Library

[39]

Nandi, A. and Jagadish, H. V. 2007. Effective phrase prediction. In Proceedings of the Conference on Very Large Databases (VLDB). 219--230.

Digital Library

[40]

Radinsky, K., Agichtein, E., Gabrilovich, E., and Markovitch, S. 2011. A word at a time: Computing word relatedness using temporal semantic analysis. In Proceedings of the International World Wide Web Conference (WWW).

Digital Library

[41]

Radinsky, K., Davidovich, S., and Markovitch, S. 2008. Predicting the news of tomorrow using patterns in Web search queries. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI).

Digital Library

[42]

Radinsky, K., Svore, K., Dumais, S., Teevan, J., Bocharov, A., and Horvitz, E. 2012. Modeling and predicting behavioral dynamics on the Web. In Proceedings of the International World Wide Web Conference (WWW).

Digital Library

[43]

Robertson, S., Zaragoza, H., and Taylor, M. 2004. Simple bm25 extension to multiple weighted fields. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM).

Digital Library

[44]

Schwarz, G. E. 1978. Estimating the dimension of a model. Ann. Stat. 2, 6, 461--464.

[45]

Shimshoni, Y., Efron, N., and Matias, Y. 2009. On the predictability of search trends. Tech. rep. Microsoft Research.

[46]

Shokouhi, M. 2011. Detecting seasonal queries by time-series analysis. In Proceedings of the Annual Special Interest Group on Information Retrieval Conference (SIGIR).

Digital Library

[47]

Shokouhi, M. and Radinsky, K. 2012. Time-sensitive query auto-completion. In Proceedings of the Annual Special Interest Group on Information Retrieval Conference (SIGIR).

Digital Library

[48]

Snyman, J. A. 2005. Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms. Springer, Berlin.

[49]

Teevan, J., Dumais, S. T., and Horvitz, E. 2005. Personalizing search via automated analysis of interests and activities. In Proceedings of the Annual Special Interest Group on Information Retrieval Conference (SIGIR).

Digital Library

[50]

Vlachos, M., Meek, C., Vagena, Z., and Gunopulos, D. 2004. Identifying similarities, periodicities and bursts for online search queries. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 131--142.

Digital Library

[51]

Wang, P., Berry, M. W., and Yang, Y. 2003. Mining longitudinal Web queries: trends and patterns. J. Amer. Soc. Inf. Sci. Techno. 54, 8, 743--758.

Digital Library

[52]

Wang, X., Zhai, C., Hu, X., and Sproat, R. 2007. Mining correlated bursty topic patterns from coordinated text streams. In Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (KDD).

Digital Library

[53]

White, R. W., Bennett, P. N., and Dumais, S. T. 2010. Predicting short-term interests using activity-based search context. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM).

Digital Library

[54]

White, R. W. and Marchionini, G. 2007. Examining the effectiveness of real-time query expansion. J. Inf. Process. Manage. 43, 3, 685--704.

Digital Library

[55]

Yue, Y., Patel, R., and Roehrig, H. 2010. Beyond position bias: Examining result attractiveness as a source of presentation bias in clickthrough data. In Proceedings of International World Wide Web Conference (WWW).

Digital Library

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Behavioral dynamics on the web: Learning, modeling, and prediction

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 31, Issue 3

July 2013

202 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/2493175

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Copyright © 2013 ACM.

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Publication History

Published: 05 August 2013

Accepted: 01 March 2013

Revised: 01 January 2013

Received: 01 May 2012

Published in TOIS Volume 31, Issue 3

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Ju XLu JLuo XZhou GWang SLi SYang Y(2022)Interest Points Analysis for Internet Forum Based on Long-Short Windows SimilarityComputers, Materials & Continua10.32604/cmc.2022.02669872:2(3247-3267)Online publication date: 2022
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Patro GPorcaro LMitchell LZhang QZehlike MGarg N(2022)Fair ranking: a critical review, challenges, and future directionsProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency10.1145/3531146.3533238(1929-1942)Online publication date: 21-Jun-2022
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