基于深层和浅层特征学习的广告点击率预测方法

Zai Huang, Zhen Pan, Qi Liu, Bai Long, Haiping Ma, Enhong Chen
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引用次数: 12

摘要

在网络广告中,点击率(CTR)预测是一项至关重要的任务,因为它可能有利于在线广告的排名和定价。据我们所知,大多数现有的CTR预测方法是浅层模型(例如,逻辑回归和分解机器)或深层模型(例如,神经网络)。不幸的是,浅层模型不能捕获或利用广告数据中的高阶非线性特征。另一方面,深层模型的计算复杂度较高,不能满足在线高效更新CTR模型的需要。为了解决上述缺点,本文提出了一种基于深层和浅层特征学习(DSL)的新型混合方法。在DSL中,我们使用深度神经网络作为离线训练的深层模型来学习高阶非线性特征,并使用Factorization Machines作为CTR预测的浅层模型。此外,我们还开发了一种基于DSL的在线学习实现,即在线edsl。与几种最先进的基线相比,在大规模真实数据集上进行的大量实验清楚地验证了我们的DSL方法和在线edsl算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Ad CTR Prediction Method Based on Feature Learning of Deep and Shallow Layers
In online advertising, Click-Through Rate (CTR) prediction is a crucial task, as it may benefit the ranking and pricing of online ads. To the best of our knowledge, most of the existing CTR prediction methods are shallow layer models (e.g., Logistic Regression and Factorization Machines) or deep layer models (e.g., Neural Networks). Unfortunately, the shallow layer models cannot capture or utilize high-order nonlinear features in ad data. On the other side, the deep layer models cannot satisfy the necessity of updating CTR models online efficiently due to their high computational complexity. To address the shortcomings above, in this paper, we propose a novel hybrid method based on feature learning of both Deep and Shallow Layers (DSL). In DSL, we utilize Deep Neural Network as a deep layer model trained offline to learn high-order nonlinear features and use Factorization Machines as a shallow layer model for CTR prediction. Furthermore, we also develop an online learning implementation based on DSL, i.e., onlineDSL. Extensive experiments on large-scale real-world datasets clearly validate the effectiveness of our DSL method and onlineDSL algorithm compared with several state-of-the-art baselines.
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