Highlighted by Ambika Krishnamachar

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To algorithmically create a good personalized homepage means assembling one page per member profile and device from thousands of videos that may be relevant for a member and from easily tens of thousands of potential rows, each with a variable number of videos. On top of that, we need to balance several factors that often compete for precious screen real estate. Our approach to personalization and recommendation largely focuses on helping our members find something new to watch, which we call discovery. However, we also want to make it easy for a member to watch the next episode of a show or re-watch something that they watched in the past, which normally falls outside the realm of recommendation. We want our recommendations to be accurate in that they are relevant to the tastes of our members, but they also need to be diverse so that we can address the spectrum of a member’s interests versus only focusing on one. We want to be able to highlight the depth in the catalog we have in those interests and also the breadth we have across other areas to help our members explore and even find new interests. We want our recommendations to be fresh and responsive to the actions a member takes, such as watching a show, adding to their list, or rating; but we also want some stability so that people are familiar with their homepage and can easily find videos they’ve been recommended in the recent past. Finally, we need to be able to place task-oriented rows, such as “My List,” in amongst the more discovery-oriented rows.