From the course: Machine Learning and AI Foundations: Recommendations
Join today to access over 22,600 courses taught by industry experts or purchase this course individually.
Latent representations of users and products
From the course: Machine Learning and AI Foundations: Recommendations
Latent representations of users and products
- [Instructor] We can estimate how much a user will like a movie by assigning attributes to each user and each movie and then multiplying them together and adding up the results. The same calculation can be represented as a matrix multiplication problem. First, we put the user attributes in the matrix called U, in this case, five, negative two, one, negative five, and five. And then, we put the movie attributes in the matrix called M, and we use matrix multiplication to find out the user's ratings. But to do this, we have to already know the user attributes and the movie attributes. It's not easy to come up with attribute ratings for each user and each movie by hand. We need a way to come up with them automatically. Let's look at the movie rating matrix that shows how all the users in our data set have rated movies so far. This matrix is very sparse, but it gives us a lot of information. For example, we know that user ID two gave five stars to movie number one. So, based on that, we…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.