This repository contains the source code of the following article:
- An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering. Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, and Paolo Cremonesi. ECIR 2022. DOI: 10.1007/978-3-030-99736-6_45
If you use our work, please cite our work. You can click on the Cite this repository button or copy the following BibTeX snippet:
@inproceedings{conf/ecir/PerezMaureraFDC22/an-evaluation-study-of-generative-adversarial-networks-for-collaborative-filtering,
author = {Fernando Benjam{\'{i}}n {P{\'{e}}rez Maurera} and
Maurizio {Ferrari Dacrema} and
Paolo Cremonesi},
title = {An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering},
booktitle = {Advances in Information Retrieval - 44th European Conference on {IR} Research, {ECIR} 2022, Stavanger, Norway, April 10-14, 2022, Proceedings, Part {I}},
series = {Lecture Notes in Computer Science},
volume = {13185},
pages = {671--685},
publisher = {Springer},
year = {2022},
url = {https://doi.org/10.1007/978-3-030-99736-6\_45},
doi = {10.1007/978-3-030-99736-6\_45}
}See our website for more information on our research group. We are actively pursuing this research direction in evaluation and reproducibility, and we are open to collaboration with other researchers. Follow our project on ResearchGate
This repo is divided into three folders:
- evaluation-cfgan: Contains the implementation of CFGAN and the experiments presented in the article.
- recsys-framework: Contains the implementation of baselines, hyper-parameter tuning, and evaluation used in the article.
- pdf: Contains a copy of the submitted article and additional material
You'll find instructions to install this project and run the experiments in the
README inside evaluation-cfgan, in fact, all commands must be run inside
the evaluation-cfgan folder.