用GAN生成推荐系统数据，验证了合成数据的可靠性。
Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets
- 用GAN从真实数据生成合成推荐数据集。
- 合成数据在精度和召回率上与原始数据趋势一致。
- 适合评估推荐模型性能，尤其冷启动场景研究。
生成对抗网络推荐系统（GANRS）方法可生成协同过滤推荐系统的数据集。该方法源码及代表性合成数据集已公开。本文通过三个不同真实数据集生成多个合成数据集，测试了用户数量和样本量变化下的表现。选取六种先进的深度学习协同过滤模型进行对比实验，结果表明合成数据在精度和召回率指标值及变化趋势上均与原始数据保持一致。所有测试模型在合成数据上表现符合预期，可与真实数据结果进行有效比较。未来工作包括探索冷启动、数据不平衡及人口公平性等场景。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。