Many prediction tasks of real-world applications need to model multi-order
feature interactions in user's event sequence for better detection performance.
However, existing popular solutions usually suffer two key issues: 1) only
focusing on feature interactions and failing to capture the sequence influence;
2) only focusing on sequence information, but ignoring internal feature
relations of each event, thus failing to extract a better event representation.
In this paper, we consider a two-level structure for capturing the hierarchical
information over user's event sequence: 1) learning effective feature
interactions based event representation; 2) modeling the sequence
representation of user's historical events. Experimental results on both
industrial and public datasets clearly demonstrate that our model achieves
significantly better performance compared with state-of-the-art baselines.

本文关注于利用两层结构对用户事件序列建模，旨在解决仅关注特征交互或者只注意序列信息而导致性能缺陷的问题，实验证明该模型相比现有技术具有显著的性能提升。