  Over the years, explosive growth in the number of items in the catalog of
e-commerce businesses, such as Amazon, Netflix, Pandora, etc., have warranted
the development of recommender systems to guide consumers towards their desired
products based on their preferences and tastes. Some of the popular approaches
for building recommender systems, for mining user, derived input datasets, are:
content-based systems, collaborative filtering, latent-factor systems using
Singular Value Decomposition (SVD), and Restricted Boltzmann Machines (RBM). In
this project, user-user collaborative filtering, item-item collaborative
filtering, content-based recommendation, SVD, and neural networks were chosen
for implementation in Python to predict the user ratings of unwatched movies
for each user, and their performances were evaluated and compared.
