Click-Through Rate (CTR) prediction has been an indispensable component for many industrial applications, such as recommendation systems and online advertising. CTR prediction systems are usually based on multi-field categorical features, i.e., every feature is categorical and belongs to one and only one field. Modeling feature conjunctions is crucial for CTR prediction accuracy. However, it requires a massive number of parameters to explicitly model all feature conjunctions, which is not scalable for real-world production systems. In this paper, we describe a novel Field-Leveraged Embedding Network (FLEN) which has been deployed in the commercial recommender system in Meitu and serves the main traffic. FLEN devises a field-wise bi-interaction pooling technique. By suitably exploiting field information, the field-wise bi-interaction pooling captures both inter-field and intra-field feature conjunctions with a small number of model parameters and an acceptable time complexity for industrial applications. We show that a variety of state-of-the-art CTR models can be expressed under this technique. Furthermore, we develop Dicefactor: a dropout technique to prevent independent latent features from co-adapting. Extensive experiments, including offline evaluations and online A/B testing on real production systems, demonstrate the effectiveness and efficiency of FLEN against the state-of-thearts. Notably, FLEN has obtained 5.19% improvement on CTR with 1/6 of memory usage and computation time, compared to last version (i.e. NFM).

翻译：点击浏览率(CTR)预测是许多工业应用,例如建议系统和在线广告的一个不可或缺的组成部分。CTR预测系统通常基于多领域绝对特征,即每个功能都是绝对的,属于一个和唯一的领域。模型特征连接对于计算TR预测的准确性至关重要。然而,它需要大量参数来明确模拟所有特征连接,这对于现实世界生产系统来说是无法伸缩的。在本文中,我们描述了在Meitu商业推荐系统内部署的新型外地杠杆化嵌入网络(FLEN),它为主要交通服务。FLEN设计了一种面向外地的双相互作用集合技术。通过适当利用外地信息,实地的双相互作用组合对于计算对于计算预测准确性至关重要。然而,它需要大量参数来明确模拟所有特征的组合,这对于现实世界生产系统来说是无法伸缩的。我们展示了各种最新的、最先进的CTR模型(FL ) 。此外,我们开发了Dicefactor:一种流技术,用来防止独立的F- 直径(F) 生产率/直径) 的升级,从共同测试系统进行实时的测试。