- #Yet another media meta manager how to#
- #Yet another media meta manager movie#
- #Yet another media meta manager tv#
Of course, to properly learn how to personalize artwork we need to collect a lot of data to find signals that indicate when one piece of artwork is significantly better for a member.Īnother challenge is to understand the impact of changing artwork that we show a member for a title between sessions. Therefore artwork personalization sits on top of the traditional recommendation problem and the algorithms need to work in conjunction with each other. What we seek to understand is when presenting a specific piece of artwork for a title influenced a member to play (or not to play) a title and when a member would have played a title (or not) regardless of which image we presented. This means that image selection is a chicken-and-egg problem operating in a closed loop: if a member plays a title it can only come from the image that we decided to present to that member. In contrast, typical recommendation settings let us present multiple selections to a member where we can subsequently learn about their preferences from the item a member selects. One challenge of image personalization is that we can only select a single piece of artwork to represent each title in each place we present it. Each new aspect that we personalize has unique challenges personalizing the artwork we display is no exception and presents different personalization challenges. ChallengesĪt Netflix, we embrace personalization and algorithmically adapt many aspects of our member experience, including the rows we select for the homepage, the titles we select for those rows, the galleries we display, the messages we send, and so forth.
Overall, by personalizing artwork we help each title put its best foot forward for every member and thus improve our member experience. So we don’t enumerate such hand-derived rules but instead rely on the data to tell us what signals to use. Of course, not all the scenarios for personalizing artwork are this clear and obvious.
#Yet another media meta manager movie#
Someone who has watched many romantic movies may be interested in Good Will Hunting if we show the artwork containing Matt Damon and Minnie Driver, whereas, a member who has watched many comedies might be drawn to the movie if we use the artwork containing Robin Williams, a well-known comedian. Here we might personalize this decision based on how much a member prefers different genres and themes. Let us consider trying to personalize the image we use to depict the movie Good Will Hunting. To the right of the arrow is the artwork that a member would get for a particular movie that we recommend for them. On the left are three titles a member watched in the past. Consider the following examples where different members have different viewing histories. Different images cover a breadth of themes in the show to go beyond what any single image portrays.Īs inspiration, let us explore scenarios where personalization of artwork would be meaningful. This is yet another way Netflix differs from traditional media offerings: we don’t have one product but over a 100 million different products with one for each of our members with personalized recommendations and personalized visuals.Īrtwork for Stranger Things that each receive over 5% of impressions from our personalization algorithm. If we present that perfect image on your homepage (and as they say: an image is worth a thousand words), then maybe, just maybe, you will give it a try.
#Yet another media meta manager tv#
The artwork may highlight an actor that you recognize, capture an exciting moment like a car chase, or contain a dramatic scene that conveys the essence of a movie or TV show. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you.
One avenue to address this challenge is to consider the artwork or imagery we use to portray the titles. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. But the job of recommendation does not end there. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial.
By Ashok Chandrashekar, Fernando Amat, Justin Basilico and Tony Jebaraįor many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time.
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