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.
@article{arxiv.2112.15292,
title = {Neural Hierarchical Factorization Machines for User's Event Sequence Analysis},
author = {Dongbo Xi and Fuzhen Zhuang and Bowen Song and Yongchun Zhu and Shuai Chen and Dan Hong and Tao Chen and Xi Gu and Qing He},
journal= {arXiv preprint arXiv:2112.15292},
year = {2022}
}