Decompose, Then Reconstruct: A Framework of Network Structures for Click-Through Rate Prediction

Abstract

Feature interaction networks are crucial for click-through rate (CTR) prediction in many applications. Extensive studies have been conducted to boost CTR accuracy by constructing effective structures of models. However, the performance of feature interaction networks is greatly influenced by the prior assumptions made by the model designer regarding its structure. Furthermore, the structures of models are highly interdependent, and launching models in different scenarios can be arduous and time-consuming. To address these limitations, we introduce a novel framework called DTR, which redefines the CTR feature interaction paradigm from a new perspective, allowing for the decoupling of its structure. Specifically, DTR first decomposes these models into individual structures and then reconstructs them within a unified model structure space, consisting of three stages: Mask, Kernel, and Compression. Each stage of DTR’s exploration of a range of structures is guided by the characteristics of the dataset or the scenario. Theoretically, we prove that the structure space of DTR not only incorporates a wide range of state-of-the-art models but also provides potentials to identify better models. Experiments on two public real-world datasets demonstrate the superiority of DTR, which outperforms state-of-the-art models.