Se-xDeepFEFM: Combining Low-Order Feature Refinement and Interaction Intensity Evaluation for Click-Through Rate Prediction

Abstract

Click-through rate (CTR) prediction can provide considerable economic and social benefits. Few studies have considered the importance of low-order features, usually employing a simple feature interaction method. To address these issues, we propose a novel model called Senet and extreme deep field-embedded factorization machine (Se-xDFEFM) for more effective CTR prediction. We first embed the squeeze-excitation network (Senet) module into Se-xDFEFM to complete low-order feature refinement, which can better filter noisy information. Then, we implement our field-embedded factorization machine (FEFM) to learn the symmetric matrix embeddings for each field pair, along with the single-vector embeddings for each feature, which builds a firm foundation for the subsequent feature interaction. Finally, we design a compressed interaction network (CIN) to realize feature construction with definite order through a vector-wise interaction. We use a deep neural network (DNN) with the CIN to simultaneously implement effective but complementary explicit and implicit feature interactions. Experimental results demonstrate that the Se-xDFEFM model outperforms other state-of-the-art baselines. Our model is effective and robust for CTR prediction. Importantly, our model variants also achieve competitive recommendation performance, demonstrating their scalability.