Action prediction and identification from mining temporal user behaviors

Abstract

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 cannot 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. Hence, it is difficult to understand the diversity in temporal 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 capture relevant users that 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 demonstrates the temporal user behavior modeling using the ensemble algorithm significantly outperforms other methods.