TY - JOUR
KW - Collaborative Filtering
KW - Deep Learning
KW - Generative Adversarial Network
KW - Synthetic Datasets
KW - Recommendation Systems
AU - Jesús Bobadilla
AU - Abraham Gutiérrez
AB - The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS source code is available along with a representative set of generated datasets. We have tested the GANRS method by creating multiple synthetic datasets from three different real datasets taken as a source. Experiments include variations in the number of users in the synthetic datasets, as well as a different number of samples. We have also selected six state-of-the-art collaborative filtering deep learning models to test both their comparative performance and the GANRS method. The results show a consistent behavior of the generated datasets compared to the source ones; particularly, in the obtained values and trends of the precision and recall quality measures. The tested deep learning models have also performed as expected on all synthetic datasets, making it possible to compare the results with those obtained from the real source data. Future work is proposed, including different cold start scenarios, unbalanced data, and demographic fairness.
IS - In press
M1 - In press
N2 - The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS source code is available along with a representative set of generated datasets. We have tested the GANRS method by creating multiple synthetic datasets from three different real datasets taken as a source. Experiments include variations in the number of users in the synthetic datasets, as well as a different number of samples. We have also selected six state-of-the-art collaborative filtering deep learning models to test both their comparative performance and the GANRS method. The results show a consistent behavior of the generated datasets compared to the source ones; particularly, in the obtained values and trends of the precision and recall quality measures. The tested deep learning models have also performed as expected on all synthetic datasets, making it possible to compare the results with those obtained from the real source data. Future work is proposed, including different cold start scenarios, unbalanced data, and demographic fairness.
PY - 9998
SE - 1
SP - 1
EP - 10
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets
UR - https://www.ijimai.org/journal/sites/default/files/2023-10/ip2023_10_002.pdf
VL - In press
SN - 1989-1660
ER -