Visualization-of-Latent-Factors-from-Movies
The goal is to visualize and interpret a 2-dimensional latent features for movies of the given data-set. We are given the categorizations of about 1600 movies into 19 genres, and ratings of some users to a specific movie. We applied matrix factorization to the sparse ratings matrix (since not all users are going to rate all movies) to look for latent factor matrices of the movies and users. Then we used the principal component analysis (PCA) to analyze the latent factor matrix of movies and projected each movie to the two strongest latent factors. Finally, we did visualization and interpretation on each category and compared the category average of the two major latent factors.