Click-Through Rate (CTR) estimation has become one of the most fundamental tasks in many real-world applications and various deep models have been proposed. Some research has proved that FiBiNet is one of the best performance models and outperforms all other models on Avazu dataset. However, the large model size of FiBiNet hinders its wider application. In this paper, we propose a novel FiBiNet++ model to redesign FiBiNet's model structure, which greatly reduces model size while further improves its performance. One of the primary techniques involves our proposed "Low Rank Layer" focused on feature interaction, which serves as a crucial driver of achieving a superior compression ratio for models. Extensive experiments on three public datasets show that FiBiNet++ effectively reduces non-embedding model parameters of FiBiNet by 12x to 16x on three datasets. On the other hand, FiBiNet++ leads to significant performance improvements compared to state-of-the-art CTR methods, including FiBiNet.

翻译：点击率（CTR）估计已成为众多实际应用中最基础的任务之一，为此已提出了多种深度模型。研究证明，FiBiNet是最优性能模型之一，在Avazu数据集上表现优于所有其他模型。然而，FiBiNet的大模型尺寸限制了其更广泛的应用。本文提出一种新型FiBiNet++模型，重新设计了FiBiNet的模型结构，大幅减小模型尺寸的同时进一步提升了性能。其中核心技术之一是我们提出的面向特征交互的"低秩层"（Low Rank Layer），这是实现模型高压缩比的关键驱动力。在三个公开数据集上的大量实验表明，FiBiNet++将FiBiNet的非嵌入模型参数有效减少了12倍至16倍。另一方面，与包括FiBiNet在内的最先进CTR方法相比，FiBiNet++带来了显著的性能提升。