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In today’s data-driven world, recommendation systems have become an integral part of many online platforms. From e-commerce websites suggesting products you might like to streaming services recommending movies and TV shows based on your preferences, recommendation systems have revolutionized the way we discover and consume content.
One popular method for building recommendation systems is matrix factorization, and in this article, we’ll explore how to implement it using the Surprise library in Python.
What is Matrix Factorization?
Matrix factorization is a collaborative filtering technique used in recommendation systems. It works by decomposing the user-item rating matrix into two lower-dimensional matrices: a user matrix and an item matrix. These matrices capture the underlying patterns and preferences of users and items, respectively.
By multiplying these two matrices, we can reconstruct the original rating matrix and make predictions for unseen user-item pairs.
