TiSSA: A Time Slice Self-Attention Approach for Modeling Sequential User Behaviors

Modeling user behaviors as sequences provides critical advantages in predicting future user actions, such as predicting the next product to purchase or the next song to listen to, for personalized search and recommendation. Recently, recurrent neural networks (RNNs) have been adopted to leverage their power in modeling sequences. However, most of the previous RNN-based work suffers from the complex dependency problem, which may lose the integrity of highly correlated behaviors and may introduce noises derived from unrelated behaviors. In this paper, we propose to integrate a novel Time Slice Self-Attention (TiSSA) mechanism into RNNs for better modeling sequential user behaviors, which utilizes the time-interval-based gated recurrent units to exploit the temporal dimension when encoding user actions, and has a specially designed time slice hierarchical self-attention function to characterize both local and global dependency of user actions, while the final context-aware user representations can be used for downstream applications. We have performed experiments on a huge dataset collected from one of the largest e-commerce platforms in the world. Experimental results show that the proposed TiSSA achieves significant improvement over the state-of-the-art. TiSSA is also adopted in this large e-commerce platform, and the results of online A/B test further indicate its practical value.