Automatic Machine Learning for Recommender Systems

This project aims to improve recommendation systems by using advanced computer techniques called Auto
Machine Learning and Meta Machine Learning. This involves automating parts of the machine learning process,
like finding similar data and picking the best settings for the computer model. This project also aims to make it
easier for others to set up these systems by automating significant portions of the work, like deciding which
features to use. Current research methods will be analyzed and evaluated to find the best methods. Overall, the
goal is to create smarter recommender systems that can learn on their own with less human help, while also
making the recommendations more accurate and helpful for customers.

Faculty Supervisor:

Eldan Cohen

Student:

Partner:

Crossing Minds Canada Inc.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program: