VARIATIONAL BAYESIAN GRAPH CONVOLUTIONAL NETWORK FOR ROBUST COLLABORATIVE FILTERING

This paper presents a variational Bayesian graph convolutional network for robust collaborative filtering (VBGCF). Conventional graph convolutional network (GCN)-based recommendation models fully trust the observed interaction graph. However, the data used in real-world applications (e.g., video streaming services) are often incomplete and unreliable. To deal with this realistic situation, we newly introduce the probabilistic model based on variational Bayesian inference to GCN-based recommendation. VBGCF can use various generated graphs instead of the observed interaction graph to learn users? preferences. Therefore, VBGCF is not affected by incompleteness and unreliability and can provide robust recommendation. The results of experiments conducted under the realistic situation show the effectiveness of VBGCF.