# Scalable Realistic Recommendation Datasets through Fractal Expansions

arXiv: https://arxiv.org/abs/1901.08910 · HTML: https://mlsanity.com/paper/1901.08910/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2019.

## Built on by (most cited first, 7 of 7)

- [Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching](https://mlsanity.com/paper/2208.09151/) (2022)
- [SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures](https://mlsanity.com/paper/2205.04711/) (2022)
- [Synthetic Data and Simulators for Recommendation Systems: Current State and Future Directions](https://mlsanity.com/paper/2112.11022/) (2021)
- [Flexible Generation of Preference Data for Recommendation Analysis](https://mlsanity.com/paper/2407.16594/) (2024)
- [Analysis and Optimization of GNN-Based Recommender Systems on Persistent Memory](https://mlsanity.com/paper/2207.11918/) (2022)
- [Generating Diverse Synthetic Datasets for Evaluation of Real-life Recommender Systems](https://mlsanity.com/paper/2412.06809/) (2024)
- [RecPFN: Prior-Fitted Networks for In-Context-Based Recommendations](https://mlsanity.com/paper/2608.19735/) (2026)

## Abstract (arXiv)

Recommender System research suffers currently from a disconnect between the size of academic data sets and the scale of industrial production systems. In order to bridge that gap we propose to generate more massive user/item interaction data sets by expanding pre-existing public data sets. User/item incidence matrices record interactions between users and items on a given platform as a large sparse matrix whose rows correspond to users and whose columns correspond to items. Our technique expands such matrices to larger numbers of rows (users), columns (items) and non zero values (interactions) while preserving key higher order statistical properties. We adapt the Kronecker Graph Theory to user/item incidence matrices and show that the corresponding fractal expansions preserve the fat-tailed distributions of user engagements, item popularity and singular value spectra of user/item interaction matrices. Preserving such properties is key to building large realistic synthetic data sets which in turn can be employed reliably to benchmark Recommender Systems and the systems employed to train them. We provide algorithms to produce such expansions and apply them to the MovieLens 20 million data set comprising 20 million ratings of 27K movies by 138K users. The resulting expanded data set has 10 billion ratings, 864K items and 2 million users in its smaller version and can be scaled up or down. A larger version features 655 billion ratings, 7 million items and 17 million users.