From the course: Machine Learning and AI Foundations: Recommendations
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How matrix factorization works
From the course: Machine Learning and AI Foundations: Recommendations
How matrix factorization works
- [Instructor] Because the ratings matrix is equal to the result of multiplying the user attributes matrix by the movie attributes matrix, we can work backwards using matrix factorization to find values for U and M. In code, we use an algorithm called, low rank matrix factorization, to do this. Let's look at how this algorithm works. Matrix factorization is the idea that a large matrix can be broken down into smaller matrices. So, assuming that we have a large matrix of numbers, and assuming that we want to be able to find two smaller matrices that multiply together to result in that large matrix, our goal is to find two smaller matrices that satisfy that requirement. If you happen to be an expert in linear algebra, you might know that there are standard ways to factor a matrix, such as using a process called, singular value decomposition. But this is a special case where that won't work. The problem is that we only know some of the values in the large matrix. Many of the entries in…
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