Modeling and predicting behavioral dynamics on the web

WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web

Volumn , Issue , 2012, Pages 599-608

  Radinsky, Kira ^a   Svore, Krysta ^b   Dumais, Susan ^b   Teevan, Jaime ^b   Bocharov, Alex ^b   Horvitz, Eric ^b  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

^b MICROSOFT RESEARCH   (United States)

Author keywords

Behavioral analysis; Predictive behavioral models

Indexed keywords

BASELINE MODELS; BEHAVIORAL ANALYSIS; BEHAVIORAL DYNAMICS; BEHAVIORAL MODEL; CONSTRUCT MODELS; LEARNING PROCEDURES; PREDICTION MODEL; QUERY SUGGESTION; TEMPORAL MODELING; TIME VARYING; USER BEHAVIORS;

FORECASTING; LEARNING ALGORITHMS; MATHEMATICAL MODELS; SEARCH ENGINES; SIGNAL PROCESSING; WORLD WIDE WEB;

BEHAVIORAL RESEARCH;

EID: 84860864146     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2187836.2187918     Document Type: Conference Paper

References (22)

1. Z. Zheng G. Mishne J. Bai R. Zhang K. Bichner C. Liao A. Dong, Y. Chang and F. Diaz. Towards recency ranking in web search. In WSDM, 2010.
2. E. Adar, D. S. Weld, B. N. Bershad, and S. D. Gribble. Why we search: visualizing and predicting user behavior. In WWW, 2007.
3. S. M. Beitzel, E. C. Jensen, A. Chowdhury, D. Grossman, and O. Frieder. Hourly analysis of a very large topically categorized web query log. In SIGIR, 2004.
4. P. N. Bennett, K. Svore, and S. T. Dumais. Classiffcation-enhanced ranking. In WWW, 2010.
5. S. Chien and N. Immorlica. Semantic similarity between search engine queries using temporal correlation. In WWW, 2005.
6. D. P. Skinner D. G. Childers and R. C. Kemerait. The cepstrum: A guide to processing. IEEE, 65:1428-1443, 1977.
7. F. Diaz. Integration of news content into web results. In WSDM, 2009.
8. J. Ginsberg, M. Mohebbi, R. Patel, L. Brammer, M. Smolinski, and L. Brilliant. Detecting inuenza epidemics using search engine query data. Nature, 457(7232):1012-4, 2009.
9. J.Durbin and S.Koopman. Time Series Analysis by State Space Methods. Oxford University Press, 2008.
10. R. Jones and F. Diaz. Temporal proffles of queries. ACM Trans. Inf. Syst, 2004.
11. J. Kleinberg. Bursty and hierarchical structure in streams. In KDD, 2002.
12. J. Kleinberg. Temporal dynamics of on-line information systems. Data Stream Management: Processing High-Speed Data Streams. Springer, 2006.
13. Y. Koren. Collaborative ffltering with temporal dynamics. In KDD, 2009.
14. A. Kulkarni, J. Teevan, K. M. Svore, and S. T. Dumais. Understanding temporal query dynamics.
15. J.Ord R. Hyndman, A.Koehler and R.Snyder. Forecasting with Exponential Smoothing (The State Space Approach). Springer, 2008.
16. K. Radinsky, E. Agichtein, E. Gabrilovich, and S. Markovitch. A word at a time: Computing word relatedness using temporal semantic analysis. In WWW, 2011.
17. K. Radinsky, S. Davidovich, and S. Markovitch. Predicting the news of tomorrow using patterns in web search queries. In WI, 2008.
18. M. Shokouhi. Detecting seasonal queries by time-series analysis. In SIGIR, 2011.
19. Jan A. Snyman. Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms. Springer, 2005.
20. M. Vlachos, C. Meek, Z. Vagena, and D. Gunopulos. Identifying similarities, periodicities and bursts for online search queries. In SIGMOD, 2004.
21. P. Wang, M. W. Berry, and Y. Yang. Mining longitudinal web queries: trends and patterns. JASIST, 54:743-758, 2003.
22. J. Yang and J. Leskovec. Patterns of temporal variation in online media. In WSDM, 2011.