Deep Time-Aware Item Evolution Network for Click-Through Rate Prediction

Abstract

For better user satisfaction and business effectiveness, Click-Through Rate (CTR) prediction is one of the most important tasks in E-commerce. It is often the case that users' interests different from their past routines may emerge or impressions such as promotional items may burst in a very short period. In essence, such changes relate to item evolution problem, which has not been investigated by previous studies. The state-of-the-art methods in the sequential recommendation, which use simple user behaviors, are incapable of modeling these changes sufficiently. It is because, in the user behaviors, outdated interests may exist and the popularity of an item over time is not well represented. To address these limitations, we introduce time-aware item behaviors for addressing the recommendation of emerging preference. The time-aware item behavior for an item is a set of users who interact with this item with timestamps. The rich interaction information of users for an item may help to model its evolution. In this work, we propose a CTR prediction model TIEN based on the time-aware item behavior. In TIEN, by leveraging the interaction time intervals, information of similar users in a short time interval helps identify the emerging user interest of the target user. By using the sequential time intervals, the item's popularity over time can be captured in evolutionary item dynamics. Noisy users who interact with items accidentally are further eliminated thus learning robust personalized item dynamics. To the best of our knowledge, this is the first study to the item evolution problem for E-commerce CTR prediction. We conduct extensive experiments on five real-world CTR prediction datasets. The results show that the TIEN model consistently achieves remarkable improvements to the state-of-the-art methods.