At Peloton, we constantly strive to improve the member experience by highlighting personalized content that speaks to each individual user. One area of focus is our landing page, the homescreen, consisting of numerous rows of class recommendations used to captivate our users and guide them through our growing catalog of workouts. In this paper, we discuss a strategy we have used to increase the rate of workouts started from our homescreen through a Thompson sampling approach to row ranking, enhanced further with a collaborative filtering method based on user similarity calculated from workout history.