Doing Data Right: How Lessons Learned Working with Conventional Data should Inform the Future of Synthetic Data for Recommender Systems

2021/10/07 by Manel Slokom, Slokom, Manel, Martha Larson +2
Computer Science · Decision Sciences · #Data Quality and Management #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Privacy-Preserving Technologies in Data #Recommender Systems and Techniques #cs.IR

paper · pdf · doi:10.48550/arxiv.2110.03275

Contribution to the SimuRec Workshop at RecSys 2021

arxiv created 2021/10/07 · openalex publication_date 2021/10/07 · arxiv updated 2021/10/08 · openalex created_date 2021/10/11 · openalex updated_date 2026/07/28

Abstract

We present a case that the newly emerging field of synthetic data in the area of recommender systems should prioritize `doing data right'. We consider this catchphrase to have two aspects: First, we should not repeat the mistakes of the past, and, second, we should explore the full scope of opportunities presented by synthetic data as we move into the future. We argue that explicit attention to dataset design and description will help to avoid past mistakes with dataset bias and evaluation. In order to fully exploit the opportunities of synthetic data, we point out that researchers can investigate new areas such as using data synthesize to support reproducibility by making data open, as well as FAIR, and to push forward our understanding of data minimization.

Citations

• Datasheets for Datasets
• Measuring the quality of Synthetic data for use in competitions
• Bias Disparity in Recommendation Systems
• Scaling Up Collaborative Filtering Data Sets through Randomized Fractal Expansions
• Measuring Recommender System Effects with Simulated Users

Related

• Effects of Using Synthetic Data on Deep Recommender Models' Performance
• Synthetic Data and Simulators for Recommendation Systems: Current State and Future Directions
• Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding
• In Defense of Synthetic Data
• Synthetic data as meaningful data. On Responsibility in data ecosystems