mlsanityData to 14 Oct 2026

Synthetic Data and Simulators for Recommendation Systems: Current State and Future Directions

arXiv 2112.11022 ↗First posted 21 Dec 2021cs.IR cs.LGcited by 2

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Abstract (arXiv)

Synthetic data and simulators have the potential to markedly improve the performance and robustness of recommendation systems. These approaches have already had a beneficial impact in other machine-learning driven fields. We identify and discuss a key trade-off between data fidelity and privacy in the past work on synthetic data and simulators for recommendation systems. For the important use case of predicting algorithm rankings on real data from synthetic data, we provide motivation and current successes versus limitations. Finally we outline a number of exciting future directions for recommendation systems that we believe deserve further attention and work, including mixing real and synthetic data, feedback in dataset generation, robust simulations, and privacy-preserving methods.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 21 Dec 2021.

Builds on

15 of 30 references.

References · 15

  1. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,380
  2. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 807
  3. 2018Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomizationcited by 294
  4. 2017How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utilitycited by 120
  5. 2018Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Datacited by 94
  6. 2019RecSim: A Configurable Simulation Platform for Recommender Systemscited by 94
  7. 2019The Architectural Implications of Facebook's DNN-based Personalized Recommendationcited by 83
  8. 2018RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertisingcited by 65
  9. 2017Domain Randomization and Generative Models for Robotic Graspingcited by 36
  10. 2016E-commerce in Your Inbox: Product Recommendations at Scalecited by 28
  11. 2021Wide Network Learning with Differential Privacycited by 17
  12. 2017Human Interaction with Recommendation Systemscited by 15
3 more
  1. 2021ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Tablecited by 12
  2. 2019Scalable Realistic Recommendation Datasets through Fractal Expansionscited by 7
  3. 2020Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hidingcited by 4

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Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systemscited by 4
  2. 2024Generating Diverse Synthetic Datasets for Evaluation of Real-life Recommender Systemscited by 0