Synthetic Data and Simulators for Recommendation Systems: Current State
and Future Directions
- URL: http://arxiv.org/abs/2112.11022v1
- Date: Tue, 21 Dec 2021 07:29:09 GMT
- Title: Synthetic Data and Simulators for Recommendation Systems: Current State
and Future Directions
- Authors: Adam Lesnikowski, Gabriel de Souza Pereira Moreira, Sara Rabhi, Karl
Byleen-Higley
- Abstract summary: Synthetic data and simulators have the potential to markedly improve the performance and robustness of recommendation systems.
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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Empirical Privacy Evaluations of Generative and Predictive Machine Learning Models -- A review and challenges for practice [0.3069335774032178]
It is crucial to empirically assess the privacy risks associated with the generated synthetic data before deploying generative technologies.
This paper outlines the key concepts and assumptions underlying empirical privacy evaluation in machine learning-based generative and predictive models.
arXiv Detail & Related papers (2024-11-19T12:19:28Z) - Data Generation via Latent Factor Simulation for Fairness-aware Re-ranking [11.133319460036082]
Synthetic data is a useful resource for algorithmic research.
We propose a novel type of data for fairness-aware recommendation: synthetic recommender system outputs.
arXiv Detail & Related papers (2024-09-21T09:13:50Z) - Effects of Using Synthetic Data on Deep Recommender Models' Performance [0.0]
This study investigates the effectiveness of synthetic data generation in addressing data imbalances within recommender systems.
Our results show that the inclusion of generated negative samples consistently improves the Area Under the Curve (AUC) scores.
arXiv Detail & Related papers (2024-06-26T12:14:10Z) - Best Practices and Lessons Learned on Synthetic Data [83.63271573197026]
The success of AI models relies on the availability of large, diverse, and high-quality datasets.
Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns.
arXiv Detail & Related papers (2024-04-11T06:34:17Z) - Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A
Comprehensive Benchmark [56.8042116967334]
Synthetic data serves as an alternative in training machine learning models.
ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task.
This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
arXiv Detail & Related papers (2023-10-25T20:32:02Z) - Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic
Data [91.52783572568214]
Synthetic data may become a dominant force in the machine learning world, promising a future where datasets can be tailored to individual needs.
We discuss which fundamental challenges the community needs to overcome for wider relevance and application of synthetic data.
arXiv Detail & Related papers (2023-04-07T16:38:40Z) - Differentially Private Algorithms for Synthetic Power System Datasets [0.0]
Power systems research relies on the availability of real-world network datasets.
Data owners are hesitant to share data due to security and privacy risks.
We develop privacy-preserving algorithms for the synthetic generation of optimization and machine learning datasets.
arXiv Detail & Related papers (2023-03-20T13:38:58Z) - TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual
Environments [84.6017003787244]
This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets.
We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
arXiv Detail & Related papers (2022-08-16T20:46:08Z) - Synthetic Data-Based Simulators for Recommender Systems: A Survey [55.60116686945561]
This survey aims at providing a comprehensive overview of the recent trends in the field of modeling and simulation.
We start with the motivation behind the development of frameworks implementing the simulations -- simulators.
We provide a new consistent classification of existing simulators based on their functionality, approbation, and industrial effectiveness.
arXiv Detail & Related papers (2022-06-22T19:33:21Z) - S^3-Rec: Self-Supervised Learning for Sequential Recommendation with
Mutual Information Maximization [104.87483578308526]
We propose the model S3-Rec, which stands for Self-Supervised learning for Sequential Recommendation.
For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence.
Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods.
arXiv Detail & Related papers (2020-08-18T11:44:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.