An Out-of-the-Box Application for Reproducible Graph Collaborative Filtering extending the Elliot Framework

Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Tommaso Di Noia, Antonio Ferrara

Abstract: Graph convolutional networks (GCNs) are taking over collaborative filtering-based recommendation. Their message-passing schema effectively distills the collaborative signal throughout the user-item graph by propagating informative content from neighbor to ego nodes. In this demonstration, we show how to run complete experimental pipelines with six state-of-the-art graph recommendation models in Elliot (i.e., our framework for recommender system evaluation). We seek to highlight four main features, namely: (i) we support reproducibility in PyTorch Geometric (i.e., the library we use to implement the baselines); (ii) reproduced graph models span across various GCN families; (iii) we prepare a Docker image to provide a self-consistent ecosystem for the running of experiments. Codes, datasets, and a video tutorial to install and launch the application are accessible at: https://github.com/sisinflab/Graph-Demo.