In this project, we investigate how recent advances in Graph Neural Network models can impact and even improve the ability of the state-of-the-art Inductive Graph Matrix Completion (IGMC) recommender system to predict ratings in the setting of only having ratings of each user-item interaction. We show this through measuring the baseline model performance against the extensions using the RMSE scoring.

The IGMC is able to perform inductive matrix completion without any reliance on side-information. This allows the model to be highly applicable in many recommender system settings. It is also able to successfully transfer learning to other datasets with completely different recommender tasks and user bases.

We contribute 2 main extensions to the model, in particular: Graph-Normalisation and Layer Aggregation alternatives. We also extended the model visualisation and conducted meso-analysis on training examples with the greatest contributions to RMSE values. Furthermore, we explore the transfer learning capabilities of these inductive models, and benchmark the results against external datasets.