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

  • Authors:

  • Kira Radinsky;Krysta M. Svore;Susan T. Dumais;Milad Shokouhi;Jaime Teevan;Alex Bocharov;Eric Horvitz

  • Affiliations:

  • Technion--Israel Institute of Technology;Microsoft Research, Redmond, WA;Microsoft Research, Redmond, WA;Microsoft Research, Cambridge;Microsoft Research, Redmond, WA;Microsoft Research, Redmond, WA;Microsoft Research, Redmond, WA

  • Venue:
  • ACM Transactions on Information Systems (TOIS)
  • Year:
  • 2013

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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.