DHEN：一种用于大规模点击率预测的深度层次包围网络
DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction
摘要：
学习特征交互对在线广告服务的模型性能很重要。因此，大量的工作已经致力于设计有效的架构来学习功能交互。然而，我们观察到这些设计的实际性能可能因数据集而异，即使声称捕获的交互顺序相同。这表明不同的设计可能具有不同的优势，并且它们捕获的交互具有不重叠的信息。出于这种观察，我们提出了DHEN -一个深层次的集成架构，可以利用异构的交互模块的优势，并学习不同顺序下的层次结构的交互。为了克服DHEN的深层和多层结构在训练中带来的挑战，我们提出了一种新的协同设计的训练系统，可以进一步提高DHEN的训练效率。DHEN在CTR预测任务的大规模数据集上的实验结果表明，DHEN在预测的归一化熵（NE）上提高了0.27%，训练吞吐量比现有基线提高了1.2倍，证明了其在实践中的有效性。
英文摘要：
Learning feature interactions is important to the model performance of online advertising services. As a result, extensive efforts have been devoted to designing effective architectures to learn feature interactions. However, we observe that the practical performance of those designs can vary from dataset to dataset, even when the order of interactions claimed to be captured is the same. That indicates different designs may have different advantages and the interactions captured by them have non-overlapping information. Motivated by this observation, we propose DHEN - a deep and hierarchical ensemble architecture that can leverage strengths of heterogeneous interaction modules and learn a hierarchy of the interactions under different orders. To overcome the challenge brought by DHEN's deeper and multi-layer structure in training, we propose a novel co-designed training system that can further improve the training efficiency of DHEN. Experiments of DHEN on large-scale dataset from CTR prediction tasks attained 0.27\% improvement on the Normalized Entropy (NE) of prediction and 1.2x better training throughput than state-of-the-art baseline, demonstrating their effectiveness in practice.
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。
