面向展示广告的点击率预测模型综述

摘要/Abstract

摘要： 点击率预测模型的研究近年来备受学术界和工业界的关注。针对展示广告定向投放的点击率预测模型,研究了样本特征的预处理技术、基于传统机器学习模型的CTR预测方案、基于最新的深度学习模型的CTR预测方案、CTR预测模型的主要性能评价指标等,并基于一个开放数据集对其中的典型方案给出性能对比和量化分析,最后讨论了目前面向展示广告的点击率预测模型研究存在的问题和未来发展趋势。

关键词: 点击率预测, 定向广告, 逻辑回归, 深度学习, 因子分解机

Abstract: In recent years,the study of the click-through rate prediction model has attracted much attention from academia and industry.As for the existing CTR prediction models for displaying targeted advertising,this paper studied the preprocessing techniques for features of samples,the CTR prediction schemes based on traditional machine learning models and the latest deep learning models,and the main performance evaluation indexes of CTR prediction models.Specially,these typical CTR prediction schemes were evaluated based on a public dataset,further some quantitative analysis and performance comparison were given.Finally,the problems and research trends in CTR prediction were discussed.

Key words: Click-through rate prediction, Deep learning, Factorization machine, Logistic regression, Targeted advertising

中图分类号:

TP311

刘梦娟,曾贵川,岳威,仇笠舟,王加昌. 面向展示广告的点击率预测模型综述[J]. 计算机科学, 2019, 46(7): 38-49. https://doi.org/10.11896/j.issn.1002-137X.2019.07.006

LIU Meng-juan,ZENG Gui-chuan,YUE Wei,QIU Li-zhou,WANG Jia-chang. Review on Click-through Rate Prediction Models for Display Advertising[J]. Computer Science, 2019, 46(7): 38-49. https://doi.org/10.11896/j.issn.1002-137X.2019.07.006

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