Neural Hierarchical Factorization Machines for User's Event Sequence Analysis. (arXiv:2112.15292v1 [cs.LG])

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 …

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