论文标题
fibinet ++：CTR预测的低等级特征交互层减小模型大小
FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction
论文作者
论文摘要
点击率（CTR）估计已成为许多实际应用中最基本的任务之一，并且已经提出了各种深层模型。一些研究证明，纤维是最好的性能模型之一，并且在Avazu数据集上的所有其他模型都胜过。但是，纤维的较大型号大小阻碍了其更广泛的应用。在本文中，我们提出了一种新颖的纤维++模型来重新设计纤维结构，该模型结构大大降低了模型大小，同时进一步提高了其性能。主要技术之一涉及我们提出的集中在特征相互作用上的“低等级层”，这是实现模型卓越压缩比的关键驱动力。在三个公共数据集上进行的广泛实验表明，在三个数据集上，纤维纤维++有效地将纤维的非安装模型参数减少到12倍至16倍。另一方面，与包括纤维的最新CTR方法相比，纤维++可以显着改善性能。
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.
