Rotative Factorization Machines

Feature interaction learning (FIL) focuses on capturing the complex relationships among multiple features for building predictive models, which is widely used in real-world tasks. Despite the research progress, existing FIL methods suffer from two major limitations. Firstly, they mainly model the feature interactions within a bounded order (e.g., small integer order) due to the exponential growth of the interaction terms. Secondly, the interaction order of each feature is often independently learned, which lacks the flexibility to capture the feature dependencies in varying contexts.