Variational Bayesian Graph Convolutional Network for Robust Collaborative Filtering

Nozomu Onodera; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama

ICASSP 2022accepted0 citations

Variational Bayesian Graph Convolutional Network for Robust Collaborative Filtering

Nozomu Onodera, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama

Abstract

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 the incompleteness and the unreliability and can provide robust recommendation. The results of experiments conducted under the realistic situation show the effectiveness of VBGCF.

BibTeX

@inproceedings{icassp2022_variationalbayes,
  title = {Variational Bayesian Graph Convolutional Network for Robust Collaborative Filtering},
  author = {Nozomu Onodera and Keisuke Maeda and Takahiro Ogawa and Miki Haseyama},
  booktitle = {ICASSP 2022},
  year = {2022}
}