Movie Recommender Systems: Implementation and Performance Evaluation
Abstract
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.
Cite
@article{arxiv.1909.12749,
title = {Movie Recommender Systems: Implementation and Performance Evaluation},
author = {Mojdeh Saadati and Syed Shihab and Mohammed Shaiqur Rahman},
journal= {arXiv preprint arXiv:1909.12749},
year = {2019}
}