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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering - Supplemental Material
message: "If you use this software, please cite both the article from preferred-citation and the software itself."
type: software
authors:
- given-names: Fernando Benjamín
family-names: Pérez Maurera
email: [email protected]
affiliation: Politecnico di Milano & ContentWise
orcid: "https://orcid.org/0000-0001-6578-7404"
- given-names: Maurizio
family-names: Ferrari Dacrema
email: [email protected]
affiliation: Politecnico di Milano
orcid: "https://orcid.org/0000-0001-7103-2788"
- given-names: Paolo
family-names: Cremonesi
email: [email protected]
affiliation: Politecnico di Milano
orcid: "https://orcid.org/0000-0002-1253-8081"
identifiers:
- type: doi
value: 10.1007/978-3-030-99736-6_45
description: Publisher version of the paper
- type: other
value: "arXiv:2201.01815"
description: ArXiv preprint of the paper
- type: doi
value: 10.5281/zenodo.5819029
description: The concept DOI of the work
repository-code: https://github.com/recsyspolimi/ecir-2022-an-evaluation-of-GAN-for-CF
abstract: >-
This repository contains the source code and data used in our experiments described in the paper
"An evaluation of Generative Adversarial Networks for Collaborative Filtering".
Refer to the README file to run our experiments.
keywords:
- evaluation
- collaborative-filtering
- generative-adversarial-networks
- recommender-systems
license: AGPL-3.0
preferred-citation:
doi: 10.1007/978-3-030-99736-6_45
authors:
- given-names: Fernando Benjamín
family-names: Pérez Maurera
email: [email protected]
affiliation: Politecnico di Milano & ContentWise
orcid: "https://orcid.org/0000-0001-6578-7404"
- given-names: Maurizio
family-names: Ferrari Dacrema
email: [email protected]
affiliation: Politecnico di Milano
orcid: "https://orcid.org/0000-0001-7103-2788"
- given-names: Paolo
family-names: Cremonesi
email: [email protected]
affiliation: Politecnico di Milano
orcid: "https://orcid.org/0000-0002-1253-8081"
title: >-
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering
abstract: >-
This work explores the reproducibility of CFGAN. CFGAN and its family of models (TagRec, MTPR, and CRGAN) learn to generate personalized and fake-but-realistic rankings of preferences for top-N recommendations by using previous interactions. This work successfully replicates the results published in the original paper and discusses the impact of certain differences between the CFGAN framework and the model used in the original evaluation. The absence of random noise and the use of real user profiles as condition vectors leaves the generator prone to learn a degenerate solution in which the output vector is identical to the input vector, therefore, behaving essentially as a simple autoencoder. The work further expands the experimental analysis comparing CFGAN against a selection of simple and well-known properly optimized baselines, observing that CFGAN is not consistently competitive against them despite its high computational cost. To ensure the reproducibility of these analyses, this work describes the experimental methodology and publishes all datasets and source code.
keywords:
- Generative Adversarial Networks
- Recommender Systems
- Collaborative Filtering
- Reproducibility
type: conference-paper
month: 4
year: 2022
collection-doi: 10.1007/978-3-030-99736-6
collection-title: Advances in Information Retrieval
collection-type: proceedings
isbn: 978-3-030-99736-6
date-published: "2022-04-05"
pages: 15
start: 671
end: 685
repository-code: "https://github.com/recsyspolimi/ecir-2022-an-evaluation-of-GAN-for-CF"
volume-title: Lecture Notes in Computer Science
volume: 13185
publisher:
name: Springer International Publishing
conference:
name: "44th European Conference on Information Retrieval"