Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions

Understand

Various factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models.

• They generally enumerate all the cross features under a predefined maximum order, and then identify useful feature interactions through model training, which suffer from two drawbacks.
• First, they have to make a trade-off between the expressiveness of higher-order cross features and the computational cost, resulting in suboptimal predictions.
• Second, enumerating all the cross features, including irrelevant ones, may introduce noisy feature combinations that degrade model performance.