Enhancing Feature Modeling for CTR Prediction: A Hybrid Strategy

The core challenge of CTR prediction lies in how to effectively model feature semantics and their complex nonlinear interactions from high-dimensional sparse data. Existing methods mostly adopt static embedding in feature representation, ignoring the contextual dependency of semantics. In interaction modeling, traditional MLP suffers from gradient forgetting and low parameter efficiency, and are difficult to efficiently fit complex patterns such as non monotonicity. In response to the aforementioned limitations, this study proposes EFMNet, a hybrid architecture that integrates dynamic representation learning and frequency domain function approximation. Firstly, a feature dynamic enhancement module is designed, which generates context-aware complementary features through FRNet to reconstruct semantics, and uses SENet to evaluate the contribution at the channel level. Through affine transformation, it generates sample adaptive dynamic embedding. On this basis, an interaction paradigm of implicit and explicit collaboration is constructed: Fourier KAN is introduced as the implicit backbone, and its learnable edge functions based on Fourier basis are utilized to fit the nonlinear dependencies among features with high parameter efficiency; at the same time, CrossNet is integrated to explicitly model higher-order cross correlations, ensuring the effective transmission of key combined signals. Experiments on the Criteo and Avazu benchmarks show that EFMNet performs obviously better than strong baselines such as EulerNet and FinalMLP (e.g Criteo_AUC: 0.8164, Criteo_Logloss: 0.4427, Avazu_AUC: 0.7881, Avazu_Logloss: 0.3684), and its efficiency is comparable to that of the widely deployed DeepFM in the industry. This work validates the effectiveness of dynamic feature modeling and frequency domain approximation in CTR prediction, providing new ideas for recommendation system design.