DCN V2：改进的深度和交叉网络以及网络规模学习排序系统的实践课程
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
摘要：
学习有效的特征交叉是构建推荐系统的关键。然而，稀疏和大的特征空间需要穷举搜索来识别有效的交叉。提出了深度交叉网络（DCN）来自动有效地学习有界度预测特征交互。不幸的是，在具有数十亿训练样本的网络规模流量模型中，DCN在学习更具预测性的特征交互时，在其交叉网络中表现出有限的表达能力。尽管取得了重大的研究进展，但生产中的许多深度学习模型仍然依赖于传统的前馈神经网络来低效地学习特征交叉。根据DCN和现有特征交互学习方法的优点/缺点，我们提出了一个改进的框架DCN-V2，使DCN在大规模工业环境中更实用。在一项广泛的超参数搜索和模型调整的综合实验研究中，我们观察到DCN-V2方法在流行的基准数据集上的性能优于所有最先进的算法。改进后的DCN-V2在特征交互学习方面更具表现力，但仍然具有成本效益，特别是当与低秩体系结构的混合物相结合时。DCN-V2很简单，可以很容易地作为构建块采用，并且在Google的许多网络规模的学习排序系统中提供了显着的离线准确性和在线业务指标收益。
英文摘要：
Learning effective feature crosses is the key behind building recommender systems. However, the sparse and large feature space requires exhaustive search to identify effective crosses. Deep & Cross Network (DCN) was proposed to automatically and efficiently learn bounded-degree predictive feature interactions. Unfortunately, in models that serve web-scale traffic with billions of training examples, DCN showed limited expressiveness in its cross network at learning more predictive feature interactions. Despite significant research progress made, many deep learning models in production still rely on traditional feed-forward neural networks to learn feature crosses inefficiently. In light of the pros/cons of DCN and existing feature interaction learning approaches, we propose an improved framework DCN-V2 to make DCN more practical in large-scale industrial settings. In a comprehensive experimental study with extensive hyper-parameter search and model tuning, we observed that DCN-V2 approaches outperform all the state-of-the-art algorithms on popular benchmark datasets. The improved DCN-V2 is more expressive yet remains cost efficient at feature interaction learning, especially when coupled with a mixture of low-rank architecture. DCN-V2 is simple, can be easily adopted as building blocks, and has delivered significant offline accuracy and online business metrics gains across many web-scale learning to rank systems at Google.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
