Our code for building a recommender system using DGL was published on GitHub: GNN-RecSys. It might be useful for others that are tackling the recommendation task.
Some specificities that might be interesting:
- Our graph was heterogeneous, with 3 node types and 5 edge types
- Our nodes had initial features
- We used a message passing paradigm similar to GraphSAGE
- We used pytorch as backend, with custom hyperparameter tuning
- We used max-margin loss with negative sampling, predicting future interactions with previous interactions
Thanks to DGL for the library, hoping this repository can help other users!