Building a Recommender System using Matrix Factorization

A huge user-item interaction matrix is broken down into lower-dimensional matrices that reflect underlying patterns in user preferences and item features in order to build a recommender system via matrix factorisation. By splitting the original matrix into two smaller matrices—one representing people and the other representing items—matrix factorisation techniques like Alternating Least Squares (ALS) and Singular Value Decomposition (SVD) can uncover latent factors that affect user decisions. These matrices can be used to forecast missing interactions because each entry represents the affinity between a user and an object. The decomposed matrices, for instance, will record users’ inclinations for particular genres or topics and the corresponding qualities of films if a movie-rating matrix is employed.

Since matrix factorisation works well with sparse datasets—where many users haven’t interacted with the majority of the items—it is frequently employed in collaborative filtering-based recommendation systems. By forecasting ratings or interaction probabilities for unrated items, the model can produce personalised suggestions after training. These recommendations can then be sorted and sent to each user individually. This method is frequently used in content recommendation platforms, streaming services, and online stores where knowing and anticipating consumer preferences is essential to improving user experience and engagement.