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Faculty of Business and Information Technology Project Summaries

Supervisors  

Amirali Abari | 

 

Supervisor name: Amirali Abari

Project title: Graph Neural Networks for Recommender Systems

Summary of research project: Recommender systems are prevalent in our day-to-day lives. They intelligently recommend desirable options to us (e.g., books on Amazon, movies on Netflix), which are consistent with our own tastes or preferences. Several recent developments have been made in applying deep learning in recommender systems. A promising direction is to utilize graph embedding and graph convolutional networks, empowering recommender systems with graph-structured side information (e.g., social networks). Our goal is to develop practical deep learning algorithms for large-scale recommender systems in the presence of graph-structured side information.

Student responsibilities/tasks:

  • The Student is expected to review the relevant research literature along with other team members.
  • The Student will also be heavily involved in the development of the AI technologies and will co-author the consequent paper written from their involvement in this Project.
  • This Project provides a unique opportunity for the candidate to foster their knowledge in AI, graph neural networks and deep learning.

Student qualifications required:

  • The Student is expected to have familiarity with PyTorch and Deep Learning and be excellent in programming in Python.

Expected training/skills to be received by the Student:

  • Graph Neural Networks.
  • Programming in PyG.
  • Literature Review.
  • Academic Writing.

Length of award: 14 Weeks

Location of award: In-Person

Available Award: NSERC USRA