网络广告点击率预估的特征学习及技术研究进展

大数据时代有效预估网络广告点击率，对企业精准营销和提高投资回报率具有至关重要的作用。对网络广告点击率预估的特征学习及技术研究进行了综述，从原始数据特点及解决方法、点击率预估的特征学习、点击率预估模型构建、评价指标选取等方面，分析了网络广告点击率预估的国内外研究现状。点击率预估可应用于互联网广告投放、推荐系统等多个领域，具有较高的研究价值。

A survey on feature learning and technologies of online advertising click-through rate estimation.

How to effectively estimate the click-through rate (CTR) of online advertising in the era of big data plays a crucial role in the accurate marketing of enterprises and the improvement of return on investment (ROI). It also helps improve user experience in real online advertising scenarios. This paper reviews the feature learning and technologies of online advertising click-through rate estimation. Specifically, in the four aspects of the abundant historical data characteristics and their corresponding solutions, feature engineering, the construction of click-rate estimation model, and the selection of evaluation indicators, the current state of domestic and international research of online advertising click-through rate estimation is analyzed. It shows that the click-through rate estimation can be applied to many areas such as internet advertising and recommendation systems, and has high research value. However, the problem of conversion rate estimation in online advertising is different from that of click-through rate estimation, and further researches are needed.