How Neighborhood Exploration influences Novelty and Diversity in Graph Collaborative Filtering

This is the official implementation of the paper How Neighborhood Exploration influences Novelty and Diversity in Graph Collaborative Filtering, accepted as short paper at MORS@RecSys2022.

This repository is heavily dependent on the framework Elliot, so we suggest you refer to the official GitHub page and documentation.

All graph models are implemented in PyTorch Geometric using the version 1.10.2, with CUDA 10.2 and cuDNN 8.0.

Installation guidelines: scenario #1

If you have the possibility to install CUDA on your workstation (i.e., 10.2), you may create the virtual environment with the requirements files we included in the repository, as follows:

# PYTORCH ENVIRONMENT (CUDA 10.2, cuDNN 8.0)

$ python3 -m venv venv_pt
$ source venv_pt/bin/activate
$ pip install --upgrade pip
$ pip install -r requirements_pt.txt
$ pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cu102.html

Installation guidelines: scenario #2

A more convenient way of running experiments is to instantiate a docker container having CUDA 10.2 already installed.

Make sure you have Docker and NVIDIA Container Toolkit installed on your machine (you may refer to this guide).

Then, you may use the following Docker image to instantiate the container equipped with CUDA 10.2:

Container Docker with CUDA 10.2 and cuDNN 8.0 (the environment for PyTorch): link

After the setup of your Docker containers, you may follow the exact same guidelines as scenario #1.

Datasets

At ./data/ you may find all tsv files for the datasets, i.e., training, validation, and test sets.

Training and testing models

To train and evaluate models an all considered metrics, you may run the following command:

$ python -u start_experiments.py --model <model-name> --dataset <dataset-name>

where <model-name> and <dataset-name> refer to the name of the model to be run and the dataset on which to run the experiment, respectively.

You may find all configutation files at ./config_files/<model-name>/<dataset-name>.yml, where all hyperparameter spaces and the exploration strategies are reported.

[IMPORTANT] for graph models with explicit message-passing, you may refer to configuration files contained in the folder pattern ./config_files/<layer-n>/<dataset-name>.yml, where n is the number of explored hops. In this way, you will run all graph models with exploration at n hops. Conversely, models without explicit message-passing follow the usual notation as explained above.

Results about calculated metrics are available in the folder ./results/<dataset-name>/performance/. Specifically, you need to access the tsv file having the following name pattern: rec_cutoff_<cutoff>_relthreshold_0_<datetime-experiment-end>.tsv.