Simple and Scalable Response Prediction for Display Advertising

作者：Olivier Chapelle, Eren Manavoglu, Rómer Rosales · 发表于：ACM Transactions on Intelligent Systems and Technology · 年份：2014 · DOI：10.1145/2532128 · 被引用次数：328 · 研究领域：Image and Video Quality Assessment、Advanced Bandit Algorithms Research、Recommender Systems and Techniques

Clickthrough and conversation rates estimation are two core predictions tasks in display advertising. We present in this article a machine learning framework based on logistic regression that is specifically designed to tackle the specifics of display advertising. The resulting system has the following characteristics: It is easy to implement and deploy, it is highly scalable (we have trained it on terabytes of data), and it provides models with state-of-the-art accuracy.