广告点击率估算技术综述

Techniques for estimating click-through rates of Web advertisements:A survey

计算广告是根据给定的用户和网页内容,通过计算得到与之最匹配的广告并进行精准定向投放的一种广告投放机制.广告的点击率预测是指利用点击日志预测的点击率,其结果受到广告的自身性质、广告位置、页面信息、用户性质,以及广告主信誉等诸多因素的影响.有效地预测广告的点击率,对于提高广告投放的效率有着至关重要的作用.本文介绍了广告点击率预测的常用模型,包括历史数据丰富的广告点击率预测模型、新广告和稀疏广告的点击率估算模型和点击率预测的优化模型,并通过真实数据集举例说明了其实现的方法.

Computational advertising is a kind of advertising mechanism which has the capability to find the most suitable ads for given users and web content, so as to advertises them accurately. Therefore, estimating click-through rate （CTR） precisely makes significant difference in the efficiency of advertising on the Internet. Ad click-through rate prediction is to estimate CTR with click log, which is influenced by the nature features of ad, the position, the page information, user properties, the reputation of advertisers and such other factors. This paper is aimed to illus- trate useful CTR prediction models, including CTR models for ads of abundant history data, CTR models for rare ads or new ads and some optimization models. Finally, the implementation methods with real data set were demonstrated as examples.