Personalising Learning
22 AI Speak: How Youtube Learns You, part 1
Models and Recommendation
ACTIVITY
These are the credit card transactions of John Doe and Tom Harry, two men living in Nantes, France. They are looking for things to do this weekend. What will you recommend to them?
List to choose from:
- The new Burger King outlet
- An olive oil tasting event
- An online luggage store
- A river-side concert
- Baby swimming class
Recommendation systems have been around at least as long as tourist guides and top-ten lists. While The Guardian Best Books of 2022 recommends the same list to everyone, you would likely adapt it when choosing for yourself – pick a few and change the order of reading based on your personal preferences.
How to recommend options for strangers? In the activity above, you probably tried to imagine their personalities based on the given information: you made judgements and applied stereotypes. Then, once you had an idea of their type, you chose from the list things that could (or not) be relevant to them. Recommenders such as Amazon, Netflix and Youtube follow a similar process.
Nowadays, whenever someone is searching for information or looking to discover online content, they use some kind of personalised recommender system1,2. The main function of Youtube is to suggest to its users what to watch amongst all the videos available on the platform. For signed-in users, it uses their past activity to create a model, or a personality type. Once it has a model for John, it can see who else has models similar to his. It then recommends to John videos similar to what he has watched and those similar to what others like him have watched.
What is a model?
Models can be used to mimic anything from users to videos to lessons a child has to learn. A model is a simplified representation of the world, so a machine can pretend to understand it:
How Youtube learns who you are
All recommendation problems involve asking a surrogate question: “What to recommend” is too general and vague for an algorithm. Netflix asked developers what will be the rating user A would give video B, considering their ratings for other videos. Youtube asks what the watch time would be for a given user in a particular context. The choice of what to ask – and predict – has a big impact on what recommendation is shown3. The idea is that the correct prediction will lead to a good recommendation. The prediction itself is based on other users with a history of similar tastes4. That is, users whose models are similar.
User models
Youtube splits the task of recommendation into two parts and uses different models for each3. We, however, will stick to a simpler explanation here.
For creating a user model, its developers have to ask, what data is relevant to video recommendation. What about what the user has watched before? What about their reviews, ratings, and explicit preferences thus far? What did they search for? Youtube uses signals that are more implicit than explicit, s