Recommendation Systems - Brief overview of Matrix Factorization
The goal of recommendation systems is to predict the rating that a user will give an item that they haven't seen yet. One type of recommendation system is known as collaborative filtering, where we try to infer the preferences of a user by filling in the missing items in a user-item matrix. The User-Item Matrix: Suppose we had a user-item matrix like the one below, where each cell represents what a given user (u ) has rated an item ( i ). There are several missing entries in this matrix, which represent the ratings that we want to predict, and eventually recommend to the user. If you were working at Netflix, these ratings could mean the number of stars a user has rated a given movie. Matrix Factorization: One way of performing collaborative filtering is via matrix factorization. In matrix factorization, we try to find two low rank matrices U and V , such that when multiplied together, they recreate the original sparse matrix. ...