document: Modeling and predicting behavioral dynamics on the web 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 .
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