ABSTRACT
User behavior on the Web changes over time. For example, the queries that people issue to search engines, and the underlying informational goals behind the queries vary over time. In this paper, we examine how to model and predict this temporal user behavior. We develop a temporal modeling framework adapted from physics and signal processing that can be used to predict time-varying user behavior using smoothing and trends. We also explore other dynamics of Web behaviors, such as the detection of periodicities and surprises. We develop a learning procedure that can be used to construct models of users' activities based on features of current and historical behaviors. The results of experiments indicate that by using our framework to predict user behavior, we can achieve significant improvements in prediction compared to baseline models that weight historical evidence the same for all queries. We also develop a novel learning algorithm that explicitly learns when to apply a given prediction model among a set of such models. Our improved temporal modeling of user behavior can be used to enhance query suggestions, crawling policies, and result ranking.
- 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. Google ScholarDigital Library
- E. Adar, D. S. Weld, B. N. Bershad, and S. D. Gribble. Why we search: visualizing and predicting user behavior. In WWW, 2007. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- P. N. Bennett, K. Svore, and S. T. Dumais. Classification-enhanced ranking. In WWW, 2010. Google ScholarDigital Library
- S. Chien and N. Immorlica. Semantic similarity between search engine queries using temporal correlation. In WWW, 2005. Google ScholarDigital Library
- D. P. Skinner D. G. Childers and R. C. Kemerait. The cepstrum: A guide to processing.IEEE, 65:1428--1443, 1977.Google ScholarCross Ref
- F. Diaz. Integration of news content into web results. In WSDM, 2009. Google ScholarDigital Library
- J. Ginsberg, M. Mohebbi, R. Patel, L. Brammer, M. Smolinski, and L. Brilliant. Detecting influenza epidemics using search engine query data.Nature, 457(7232):1012--4, 2009.Google ScholarCross Ref
- J.Durbin and S.Koopman.Time Series Analysis by State Space Methods. Oxford University Press, 2008.Google Scholar
- R. Jones and F. Diaz. Temporal profiles of queries. ACM Trans. Inf. Syst, 2004. Google ScholarDigital Library
- J. Kleinberg. Bursty and hierarchical structure in streams. In KDD, 2002. Google ScholarDigital Library
- J. Kleinberg. Temporal dynamics of on-line information systems. Data Stream Management: Processing High-Speed Data Streams. Springer, 2006.Google Scholar
- Y. Koren. Collaborative filtering with temporal dynamics. In KDD, 2009. Google ScholarDigital Library
- A. Kulkarni, J. Teevan, K. M. Svore, and S. T. Dumais. Understanding temporal query dynamics.Google Scholar
- J.Ord R. Hyndman, A.Koehler and R.Snyder. Forecasting with Exponential Smoothing (The State Space Approach). Springer, 2008.Google ScholarCross Ref
- K. Radinsky, E. Agichtein, E. Gabrilovich, and S. Markovitch. A word at a time: Computing word relatedness using temporal semantic analysis. In WWW, 2011. Google ScholarDigital Library
- K. Radinsky, S. Davidovich, and S. Markovitch. Predicting the news of tomorrow using patterns in web search queries. In WI, 2008. Google ScholarDigital Library
- M. Shokouhi. Detecting seasonal queries by time-series analysis. In SIGIR, 2011. Google ScholarDigital Library
- Jan A. Snyman.Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms. Springer, 2005.Google Scholar
- M. Vlachos, C. Meek, Z. Vagena, and D. Gunopulos. Identifying similarities, periodicities and bursts for online search queries. In SIGMOD, 2004. Google ScholarDigital Library
- P. Wang, M. W. Berry, and Y. Yang. Mining longitudinal web queries: trends and patterns. JASIST, 54:743--758, 2003. Google ScholarDigital Library
- J. Yang and J. Leskovec. Patterns of temporal variation in online media. In WSDM, 2011. Google ScholarDigital Library
Index Terms
- Modeling and predicting behavioral dynamics on the web
Recommendations
Learning user interaction models for predicting web search result preferences
SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrievalEvaluating user preferences of web search results is crucial for search engine development, deployment, and maintenance. We present a real-world study of modeling the behavior of web search users to predict web search result preferences. Accurate ...
Behavioral dynamics on the web: Learning, modeling, and prediction
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 ...
Studying the influence of requesters in posted-price crowdsourcing
CODS-COMAD '18: Proceedings of the ACM India Joint International Conference on Data Science and Management of DataCrowd-powered systems have recently emerged as useful models for solving complex tasks online by combining machine intelligence with crowd intelligence. These models are mainly of two types - collaborative and competitive. Studying the behavior of the ...
Comments