01805nas a2200229   4500000000100000000000100001008004100002260001200043653002800055653001800083653003500101653002300136653002700159100002100186700002300207245008900230856008100319300000900400490001300409520113900422022001401561       9998                            d  c10/202310aCollaborative Filtering10aDeep Learning10aGenerative Adversarial Network10aSynthetic Datasets10aRecommendation Systems1 aJesús Bobadilla1 aAbraham Gutiérrez00aTesting Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets  uhttps://www.ijimai.org/journal/sites/default/files/2023-10/ip2023_10_002.pdf  a1-100 vIn press3 aThe 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.  a1989-1660