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Action Prediction and Identification From Mining Temporal User Behavior

Dakan Wang and Gang Wang


Predicting user's action provides many monetization opportunities to web service providers. If a user's future action can be predicted and identified correctly in time or in advance, we can not only satisfy user's current need, but also facilitate and simplify user's future online activities. Traditional works on user behavior modeling such as implicit feedback or personalization mainly investigate on users' immediate, short-term or aggregate behaviors. As such, it is difficult to understand the diversity in user behavior and predict user's future action. In this paper, we consider a forecasting problem of temporal user behavior modeling. Our first objective is able to predict whether a user will perform an action. The second objective is able to identify whether a user has finished the action, even when the action happened offline. We propose an ensemble algorithm to achieve both objectives. The experiment compares several implementation methods and illustrates how to build the temporal behavior model to capture relevant users with a high precision.


Publication typeTechReport
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