This project was presented in a 40min talk + Q&A available on Youtube and in a Medium blog post

Graph Neural Networks for Recommender Systems
This repository contains code to train and test GNN models for recommendation, mainly using the Deep Graph Library

What kind of recommendation?
For example, an organisation might want to recommend items of interest to all users of its ecommerce platforms.

How can this repository can be used?
This repository is aimed at helping users that wish to experiment with GNNs for recommendation, by giving a real example of code
to build a GNN model, train it and serve recommendations.

No training data, experiments logs, or trained model are available in this repository.

What should the data look like?
To run the code, users need multiple data sources, notably interaction data between user and items and features of users and items.

The interaction data sources should be adjacency lists. Here is an example:

customer_id item_id timestamp click purchase
imbvblxwvtiywunh 3384934262863770 2018-01-01 0 1
nzhrkquelkgflone 8321263216904593 2018-01-01 1 0
... ... ... ... ...
cgatomzvjiizvctb 2756920171861146 2019-12-31 1 0
cnspkotxubxnxtzk 5150255386059428 2019-12-31 0 1

The feature data should have node identifier and node features:

customer_id is_male is_female
imbvblxwvtiywunh 0 1
nzhrkquelkgflone 1 0
... ... ...
cgatomzvjiizvctb 0 1
cnspkotxubxnxtzk 0 1

Run the code

There are 3 different usages of the code: hyperparametrization, training and inference.
Examples of how to run the code are presented in UseCases.ipynb.

All 3 usages require specific files to be available. Please refer to the docstring to
see which files are required.


Hyperparametrization is done using the file.
Going through the space of hyperparameters, the loop builds a GNN model, trains it on a sample of training data, and computes its performance metrics.
The metrics are reported in a result txt file, and the best model's parameters are saved in the models directory.
Plots of the training experiments are saved in the plots directory.
Examples of recommendations are saved in the outputs directory.

python --from_beginning -v --visualization --check_embedding --remove 0.85 --num_epochs 100 --patience 5 --edge_batch_size 1024 --item_id_type 'ITEM IDENTIFIER' --duplicates 'keep_all'

Refer to docstrings of for details on parameters.


When the hyperparameters are selected, it is possible to train the chosen GNN model on the available data.
This process saves the trained model in the models directory. Plots, training logs, and examples of recommendations are saved.

python --fixed_params_path test/fixed_params_example.pkl --params_path test/params_example.pkl --visualization --check_embedding --remove .85 --edge_batch_size 512

Refer to docstrings of for details on parameters.


With a trained model, it is possible to generate recommendations for all users or specific users.
Examples of recommendations are printed.

python --params_path test/final_params_example.pkl --user_ids 123456 \
--user_ids 654321 --user_ids 999 \
--trained_model_path test/final_model_trained_example.pth --k 10 --remove .99

Refer to docstrings of for details on parameters.