Reinforcement Learning based Graph Convolutional Recommender Systems

This project aims to use and experiment deep learning technique on modern recommender systems such as Graph Convolutional Network. The purpose of this implementation will be to drastically improve recommendation structure’s benchmark. This will allow extract user’s embedding by mapping from pre-existing features that describe the user such as ID and relevant attributes.
In this project students will be integrated as a member of the advanced analytics research team that includes multiple PhD holders in relevant domains.
Students would work on the following main topics:
1. Implementing Graph Convolutional Networks as a recommender system.
2. Implementing an Actor-Critic Deep Deterministic Policy Gradient models for graph-based recommender systems.

Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

Mouvement des caisses Desjardins

Discipline:

Computer science

Sector:

Finance and Insurance

University:

Université de Montréal

Program: