Ya Xu, Nanyu Chen, Adrian Fernandez, Omar Sinno, Anmol Bhasin

In the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015)


 

Abstract

A/B testing, also known as bucket testing, split testing, or controlled experiment, is a standard way to evaluate user engagement or satisfaction from a new service, feature, or product. It is widely used among online websites, including social network sites such as Facebook, LinkedIn, and Twitter to make data-driven decisions. At LinkedIn, we have seen tremendous growth of controlled experiments over time, with now over 400 concurrent experiments running per day. General A/B testing frameworks and methodologies, including challenges and pitfalls, have been discussed extensively in several previous KDD work. In this paper, we describe in depth the experimentation platform we have built at LinkedIn and the challenges that arise particularly when running A/B tests at large scale in a social network setting. We start with an introduction of the experimentation platform and how it is built to handle each step of the A/B testing process at LinkedIn, from designing and deploying experiments to analyzing them. It is then followed by discussions on several more sophisticated A/B testing scenarios, such as running offline experiments and addressing the network effect, where one user’s action can influence that of another. Lastly, we talk about features and processes that are crucial for building a strong experimentation culture.