Our objective for the Netflix recommendation engine is to create a personalized experience for our members, making it easier for them to find a video to watch and enjoy. When a member logs on to the service, she/he may be in one or a combination of different watching modes: discovering a new content to watch, continuing to watch a partially-watched movie or a TV show she/he has been binging on, playing one of the contents she/he had put in her play list during an earlier session, etc. If, for example, we can reasonably predict when a member is more likely to be in the continuation mode, and which videos she/he is more likely to resume, it makes sense to place those videos in more prominent places of the home page. In this talk we focus on understanding the discovery vs. continuation behavior and explain how we have used machine learning to improve the member experience by learning a personalized balance between those two modes. As a case study, we focus on a recent change on the personalization of a row of recommendations called “Continue Watching,” which appears on the main page of the Netflix member homepage on the website and the app and currently drives a significant proportion of member streaming hours.