https://github.com/vndee/sentrec
Sentiment Graph for Product Recommendation
https://github.com/vndee/sentrec
amazon-fine-food-reviews-dataset deep-learning graph-representation-learning recommendation-system
Last synced: 7 months ago
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Sentiment Graph for Product Recommendation
- Host: GitHub
- URL: https://github.com/vndee/sentrec
- Owner: vndee
- Created: 2021-04-22T07:29:19.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-08-02T17:35:15.000Z (about 4 years ago)
- Last Synced: 2025-01-19T07:26:38.617Z (9 months ago)
- Topics: amazon-fine-food-reviews-dataset, deep-learning, graph-representation-learning, recommendation-system
- Language: Python
- Homepage:
- Size: 220 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
### Sentiment Graph for Product Recommendation System
#### Loader:
- [x] Load un-directed graph from [Amazon Fine Food Reviews Dataset](https://snap.stanford.edu/data/web-FineFoods.html).
#### Backbone:
- [x] GCN
- [x] SAGE
- [x] R-GCN
- [x] 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.