Recommender systems via Covariance Neural Networks
Using precision matrices as Graph Collaborative Filter
V.F.G. Vansteelant (TU Delft - Electrical Engineering, Mathematics and Computer Science)
E. Isufi – Mentor (TU Delft - Multimedia Computing)
A. Cavallo – Mentor (TU Delft - Multimedia Computing)
C. Liu – Mentor (TU Delft - Multimedia Computing)
K.A. Hildebrandt – Graduation committee member (TU Delft - Computer Graphics and Visualisation)
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Abstract
This research investigates the application of Graph Neural Networks (GNNs) for rating prediction in recommender systems, utilizing precision matrices as graph filters. The focus is on movie recommendation, where graph-based structures are especially relevant due to the importance of user and item relationships. A leave-one-out masking strategy is employed during training to ensure the model learns from all available training data. The proposed model achieves a test root mean squared error (RMSE) of approximately 0.95 on the MovieLens-100k dataset, performing reasonably well compared to existing graph-based and matrix factorization methods. While the model accurately predicts average ratings, it tends to overestimate lower ratings. These results demonstrate the potential of precision-based graph filters in GNNs but also reveal significant room for improvement before reaching state-of-the-art performance. Future work may include output calibration and sparsification of the precision matrix to enhance both efficiency and predictive accuracy.