ABOUT THE PROJECT
Recommender systems are one of the most successful and widespread application of machine learning technologies in business. You can find large scale recommender systems in retail, video on demand, or music streaming. Machine learning algorithms in recommender systems are typically classified into two categories — content based and collaborative filtering methods although modern recommenders combine both approaches. Content based methods are based on similarity of item attributes and collaborative methods calculate similarity from interactions. This project is aimed at building machine learning skills for application in a variety of projects in the new future.
ABOUT THE MENTOR
Dr. Kenneth Fletcher is an Assistant Professor of Computer Science at the University of Massachusetts, Boston. He will be lending his expertise for the Machine Learning Recommender Systems project.
ABOUT THE TEAM
There are thirty one ready to learn and industrious members on this team. Sub-divided into five groups. Namely:
Data generation with variational auto encoder(VAE)
Data generation with generative adversarial networks(GANs)
Matrix Factorization recommender system
KNN recommender system
Neural Network based recommender system
THE JOURNEY SO FAR
Project data for the recommender systems were disseminated, and participants have been operationally attuned to the installation and use of Virtual engines.
Exciting technologies(to mention a few) being explored are:
Tensor Flow
Py Torch
GitLab
Lenox operating system
Ubuntu Virtual Machine
Stream lit
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