Sentiment Graph for Product Recommendation System

Loader:

• Load un-directed graph from Amazon Fine Food Reviews Dataset.

Backbone:

• GCN
• SAGE
• R-GCN
• SEAL

TODO:

• Prepare dataset (load, preprocess, dump).
• Fine-tuning pre-trained Transformers model for reviews rating prediction.

Experiment

Variant	MSE
Baseline
GCN
RGCN
SAGE
SEAL

References

• Zhang, M., & Chen, Y. (2018). Link Prediction Based on Graph Neural Networks. NeurIPS.
• Zhang, M., Li, P., Xia, Y., Wang, K., & Jin, L. (2020). Revisiting Graph Neural Networks for Link Prediction. ArXiv, abs/2010.16103.
• Schlichtkrull, M., Kipf, T., Bloem, P., Berg, R.V., Titov, I., & Welling, M. (2018). Modeling Relational Data with Graph Convolutional Networks. ESWC.
• Chiang, W., Liu, X., Si, S., Li, Y., Bengio, S., & Hsieh, C. (2019). Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
• Zeng, H., Zhou, H., Srivastava, A., Kannan, R., & Prasanna, V. (2020). GraphSAINT: Graph Sampling Based Inductive Learning Method. ArXiv, abs/1907.04931.
• Hamilton, W.L., Ying, Z., & Leskovec, J. (2017). Inductive Representation Learning on Large Graphs. NIPS.