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      CTR prediction for online advertising based on a features conjunction model

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          Abstract

          Click-through rate (CTR) predictions are important for internet companies. The CTR is closely related to the context, user attributes and advertising attributes, with effective CTR predictions essential for improving company revenue. The traditional LR model was optimized to predict the relationship between the user and advertiser characteristics for the CTR which were added to the Sigmoid function to obtain a new features conjunction model. The online optimization algorithm follow-the-regularized-leader (FTRL) was used to improve the efficiency of the parameter, and the mixed regularization was used to prevent over fitting. Tests on a real-world advertising dataset show that this method has better accuracy, efficiency, parameter sensitivity and reliability compared with previous algorithms.

          Abstract

          摘要 点击率 (click-through rate, CTR) 预测是互联网公司中重要的研究课题, 预测结果与上下文、用户属性和广告属性息息相关, CTR的有效预测对提高广告公司的收入至关重要。该文在对传统逻辑回归 (logistic regression, LR) 模型的相关原理和参数优化算法介绍的基础上, 抽离出用户特征和广告特征, 将用户与广告之间特征的关联信息添加到Sigmoid函数中得到一种特征关联模型。与以往求解方法不同, 该方法采用在线最优化算法FTRL (follow-the-regularized-leader) 提高参数计算效率, 采用混合正则化来防止训练过拟合。真实的广告数据集上的实验结果表明:该方法与传统的模型和方法相比具有更好的预测精度、效率、参数敏感性和可靠性。

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          Author and article information

          Journal
          J Tsinghua Univ (Sci & Technol)
          Journal of Tsinghua University (Science and Technology)
          Tsinghua University Press
          1000-0054
          15 April 2018
          27 April 2018
          : 58
          : 4
          : 374-379
          Affiliations
          1School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
          2Department of Mathematical Science, Zhejiang University, Hangzhou 310058, China
          3School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
          Author notes
          *Corresponding author: ZHANG Hua, E-mail: zhangh@ 123456hdu.edu.cn
          Article
          j.cnki.qhdxxb.2018.26.021
          10.16511/j.cnki.qhdxxb.2018.26.021
          Copyright © Journal of Tsinghua University

          This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 Unported License (CC BY-NC 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See https://creativecommons.org/licenses/by-nc/4.0/.

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