Generating Diverse Synthetic Datasets for Evaluation of Real-life Recommender Systems

TL;DR

This paper introduces a flexible, modular framework for generating diverse, high-quality synthetic datasets tailored for evaluating real-life recommender systems, addressing limitations of existing methods.

Contribution

The authors present a novel, open-source Python framework that enables controlled, customizable synthetic dataset generation for recommender system research.

Findings

Framework effectively isolates model behavior in diverse scenarios

Enables benchmarking and bias detection in recommender systems

Supports iterative modifications for specific experimental needs

Abstract

Synthetic datasets are important for evaluating and testing machine learning models. When evaluating real-life recommender systems, high-dimensional categorical (and sparse) datasets are often considered. Unfortunately, there are not many solutions that would allow generation of artificial datasets with such characteristics. For that purpose, we developed a novel framework for generating synthetic datasets that are diverse and statistically coherent. Our framework allows for creation of datasets with controlled attributes, enabling iterative modifications to fit specific experimental needs, such as introducing complex feature interactions, feature cardinality, or specific distributions. We demonstrate the framework's utility through use cases such as benchmarking probabilistic counting algorithms, detecting algorithmic bias, and simulating AutoML searches. Unlike existing methods that either focus narrowly on specific dataset structures, or prioritize (private) data synthesis through real data, our approach provides a modular means to quickly generating completely synthetic datasets we can tailor to diverse experimental requirements. Our results show that the framework effectively isolates model behavior in unique situations and highlights its potential for significant advancements in the evaluation and development of recommender systems. The readily-available framework is available as a free open Python package to facilitate research with minimal friction.