Data Generation via Latent Factor Simulation for Fairness-aware Re-ranking

2024/09/21 by Elena Štefancová, Stefancova, Elena, Cassidy All +9
Computer Science · Decision Sciences · #Bayesian Modeling and Causal Inference #Data Management and Algorithms #Data Quality and Management #FOS: Computer and information sciences #Information Retrieval (cs.IR)

paper · pdf · doi:10.48550/arxiv.2409.14078

openalex publication_date 2024/09/21 · openalex created_date 2024/10/26 · openalex updated_date 2026/07/28

Abstract

Synthetic data is a useful resource for algorithmic research. It allows for the evaluation of systems under a range of conditions that might be difficult to achieve in real world settings. In recommender systems, the use of synthetic data is somewhat limited; some work has concentrated on building user-item interaction data at large scale. We believe that fairness-aware recommendation research can benefit from simulated data as it allows the study of protected groups and their interactions without depending on sensitive data that needs privacy protection. In this paper, we propose a novel type of data for fairness-aware recommendation: synthetic recommender system outputs that can be used to study re-ranking algorithms.

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