Evaluating the performance of sparsified precision VNNs as a graph collaborative filter
J.J. Boon (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
Graph Neural Networks (GNNs) are an effective architecture for implementing collaborative filtering-based recommender systems. This paper evaluates the performance and computational complexity of precision matrix-based VNNs as a collaborative filter on the MovieLens-100K dataset. Results show the estimated precision matrix contains a high amount of noise when calculated from sparse data, which impacts the performance of the model. After sparsifying the precision matrix, the performance and computational complexity improved significantly.