# What Data is Really Necessary? A Feasibility Study of Inference Data Minimization for Recommender Systems

arXiv: https://arxiv.org/abs/2508.21547 · HTML: https://mlsanity.com/paper/2508.21547/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 12 of 12)

- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [Embarrassingly Shallow Autoencoders for Sparse Data](https://mlsanity.com/paper/1905.03375/) (2019)
- [A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research](https://mlsanity.com/paper/1911.07698/) (2019)
- [RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback](https://mlsanity.com/paper/1912.11160/) (2019)
- [Revisiting the Performance of iALS on Item Recommendation Benchmarks](https://mlsanity.com/paper/2110.14037/) (2021)
- [Operationalizing the Legal Principle of Data Minimization for Personalization](https://mlsanity.com/paper/2005.13718/) (2020)
- [Reviving Purpose Limitation and Data Minimisation in Data-Driven Systems](https://mlsanity.com/paper/2101.06203/) (2021)
- [Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization](https://mlsanity.com/paper/2107.08096/) (2021)
- [Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms](https://mlsanity.com/paper/1911.00936/) (2019)
- [On Sampling Collaborative Filtering Datasets](https://mlsanity.com/paper/2201.04768/) (2022)
- [Collaborative Filtering via High-Dimensional Regression](https://mlsanity.com/paper/1904.13033/) (2019)

## Abstract (arXiv)

Data minimization is a legal principle requiring personal data processing to be limited to what is necessary for a specified purpose. Operationalizing this principle for recommender systems, which rely on extensive personal data, remains a significant challenge. This paper conducts a feasibility study on minimizing implicit feedback inference data for such systems. We propose a novel problem formulation, analyze various minimization techniques, and investigate key factors influencing their effectiveness. We demonstrate that substantial inference data reduction is technically feasible without significant performance loss. However, its practicality is critically determined by two factors: the technical setting (e.g., performance targets, choice of model) and user characteristics (e.g., history size, preference complexity). Thus, while we establish its technical feasibility, we conclude that data minimization remains practically challenging and its dependence on the technical and user context makes a universal standard for data `necessity' difficult to implement.