FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction

As an important modeling paradigm in click-through rate (CTR) prediction, the Deep & Cross Network and its derivative models have gained widespread recognition, primarily due to their success in trade-off computational cost and performance. However, this paradigm typically depends on deep neural network (DNN) to implicitly learn high-order feature interactions, without explicitly modeling extremely high-order interactions due to concerns about model complexity. To address this limitation, we propose a novel model for CTR prediction, called the Fusing Cross Network (FCN), which consists of two sub-networks: the Exponential Cross Network (ECN) and the Linear Cross Network (LCN). Specifically, ECN explicitly captures extremely high-order feature interactions whose order increases exponentially with network depth, while LCN captures low-order feature interactions with linearly increasing order. By integrating these two sub-networks, FCN is able to explicitly model a broad spectrum of feature interactions, thereby eliminating the need to rely on implicit modeling by DNN. Moreover, we introduce a low-cost aggregation method that reduces the number of parameters by 50% and inference latency by 23%. Meanwhile, we propose a simple yet effective loss function, Tri-BCE, which provides tailored supervision signals for each sub-network. We evaluate the effectiveness and efficiency of FCN on six public benchmark datasets and 16 baselines. Furthermore, we verify the effectiveness of the FCN on a real-world business dataset spanning seven days. The code, running logs, and detailed hyperparameter configurations are publicly available at https://github.com/salmon1802/FCN . CCS Concepts • Information systems → Recommender systems.