论文标题
根据URL嵌入预测展示广告中的转换
Predicting conversions in display advertising based on URL embeddings
论文作者
论文摘要
由于广告购买过程的自动化，近年来在线展示广告正在迅速增长。实时投标（RTB）允许通过实时拍卖来自动交易广告商和发布者之间的广告印象。为了提高广告系列的有效性，广告商应在不久的将来向很可能会转换（即购买，注册，网站访问等）的用户提供广告。在这项研究中，我们介绍并检查了不同的模型，以估计用户转换的可能性，鉴于其访问的URL历史记录。受自然语言处理的启发，我们引入了三个URL嵌入模型来计算语义有意义的URL表示。为了证明不同提议的表示和转换预测模型的有效性，我们对从广告平台收集的实际记录事件进行了实验。
Online display advertising is growing rapidly in recent years thanks to the automation of the ad buying process. Real-time bidding (RTB) allows the automated trading of ad impressions between advertisers and publishers through real-time auctions. In order to increase the effectiveness of their campaigns, advertisers should deliver ads to the users who are highly likely to be converted (i.e., purchase, registration, website visit, etc.) in the near future. In this study, we introduce and examine different models for estimating the probability of a user converting, given their history of visited URLs. Inspired by natural language processing, we introduce three URL embedding models to compute semantically meaningful URL representations. To demonstrate the effectiveness of the different proposed representation and conversion prediction models, we have conducted experiments on real logged events collected from an advertising platform.
