Elevating CTR Prediction: Field Interaction, Global Context Integration, and High-Order Representations

Recommendation systems have been increasingly prevalent in online applications. For CTR prediction, attention based models are common as a means to efficiently learn interactions between attribute features. However, self-attention has limitations, such as not considering relationships between fields and causing partial information reflection when specific feature combinations have strong relationships. To enhance this, the research introduces interaction weights to capture field relationships and incorporates Multi-layer Perceptron (MLP) and Squeeze and Excitation Networks (SENET) to include global information. Additionally, an extra module is added to address the challenge of creating explicit high-order representations. Experimental results show that the proposed model outperforms all state-of-the-art baseline models in CTR prediction across three public datasets.