论文标题
单击率预测的现场安装分解机
Field-Embedded Factorization Machines for Click-through rate prediction
论文作者
论文摘要
点击率（CTR）预测模型在许多在线应用程序中很常见，例如数字广告和推荐系统。现场感知计算机（FFM）和现场加权分解机（FWFM）是CTR预测的浅层模型中的最先进。最近，还提出了许多基于深度学习的模型。在更深层次的模型中，DEEPFM，XDEEPFM，AUTOINT+和FIBINET是最先进的模型。更深的模型结合了一个核心体系结构组件，该组件与深度神经网络（DNN）组件一起学习明确的特征交互。我们提出了一台新型的浅田间填充分解机（FEFM）及其深层深地上的分解机（DEEPFEFM）。 FEFM学习每个字段对的对称矩阵嵌入以及每个功能的通常的单个向量嵌入。 FEFM的模型复杂性明显低于FFM，并且与FWFM的复杂性大致相同。 FEFM还具有有关重要字段和场相互作用的有见地的数学属性。 DeepFefm结合了FEFM组件学到的FEFM相互作用向量与DNN，因此能够学习高阶相互作用。我们对两个大型公开可用的现实世界数据集进行了全面的实验。在比较测试AUC和日志损耗时，结果表明FEFM和DEEPFEFM的表现优于现有的CTR预测任务的现有最新浅层和DEEP模型。我们已经在DeepCTR库（https://github.com/shenweichen/deepctr）中提供了FEFM和DeepFefm的代码。
Click-through rate (CTR) prediction models are common in many online applications such as digital advertising and recommender systems. Field-Aware Factorization Machine (FFM) and Field-weighted Factorization Machine (FwFM) are state-of-the-art among the shallow models for CTR prediction. Recently, many deep learning-based models have also been proposed. Among deeper models, DeepFM, xDeepFM, AutoInt+, and FiBiNet are state-of-the-art models. The deeper models combine a core architectural component, which learns explicit feature interactions, with a deep neural network (DNN) component. We propose a novel shallow Field-Embedded Factorization Machine (FEFM) and its deep counterpart Deep Field-Embedded Factorization Machine (DeepFEFM). FEFM learns symmetric matrix embeddings for each field pair along with the usual single vector embeddings for each feature. FEFM has significantly lower model complexity than FFM and roughly the same complexity as FwFM. FEFM also has insightful mathematical properties about important fields and field interactions. DeepFEFM combines the FEFM interaction vectors learned by the FEFM component with a DNN and is thus able to learn higher order interactions. We conducted comprehensive experiments over a wide range of hyperparameters on two large publicly available real-world datasets. When comparing test AUC and log loss, the results show that FEFM and DeepFEFM outperform the existing state-of-the-art shallow and deep models for CTR prediction tasks. We have made the code of FEFM and DeepFEFM available in the DeepCTR library (https://github.com/shenweichen/DeepCTR).
