Recommending for Long-Term Member Satisfaction at Netflix
Our mission at Netflix is to entertain the world. Our personalization algorithms play a crucial role in delivering on this mission for all members by recommending the right shows, movies, and games at the right time. This goal extends beyond immediate engagement; we aim to create an experience that brings lasting enjoyment to our members. Traditional recommender systems often optimize for short-term metrics like clicks or engagement, which may not fully capture long-term satisfaction. We strive to recommend content that not only engages members in the moment but also enhances their long-term satisfaction, which increases the value they get from Netflix, and thus they’ll be more likely to continue to be a member.
One simple way we can view recommendations is as a contextual bandit problem. When a member visits, that becomes a context for our system and it selects an action of what recommendations to show, and then the member provides various types of feedback. These feedback signals can be immediate (skips, plays, thumbs up/down, or adding items to their playlist) or delayed (completing a show or renewing their subscription). We can define reward functions to reflect the quality of the recommendations from these feedback signals and then train a contextual bandit policy on historical data to maximize the expected reward.
There are many ways that a recommendation model can be improved. They may come from more informative input features, more data, different architectures, more parameters, and so forth. In this post, we focus on a less-discussed aspect about improving the recommender objective by defining a reward function that tries to better reflect long-term member satisfaction.
