Neural Hierarchical Factorization Machines for User's Event Sequence Analysis
This addresses prediction tasks in real-world applications like recommendation systems by improving detection performance, though it appears incremental as it builds on existing factorization machine and sequence modeling approaches.
The paper tackles the problem of modeling multi-order feature interactions in user event sequences for prediction tasks, proposing a two-level structure that captures both feature interactions within events and sequence patterns across events, achieving significantly better performance than state-of-the-art baselines on industrial and public datasets.
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.