# APS Explorer: Navigating Algorithm Performance Spaces for Informed Dataset Selection

arXiv: https://arxiv.org/abs/2508.19399 · HTML: https://mlsanity.com/paper/2508.19399/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2025.

## Built on by (most cited first, 2 of 2)

- [Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems](https://mlsanity.com/paper/2605.02379/) (2026)
- [Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies](https://mlsanity.com/paper/2606.07492/) (2026)

## Abstract (arXiv)

Dataset selection is crucial for offline recommender system experiments, as mismatched data (e.g., sparse interaction scenarios require datasets with low user-item density) can lead to unreliable results. Yet, 86% of ACM RecSys 2024 papers provide no justification for their dataset choices, with most relying on just four datasets: Amazon (38%), MovieLens (34%), Yelp (15%), and Gowalla (12%). While Algorithm Performance Spaces (APS) were proposed to guide dataset selection, their adoption has been limited due to the absence of an intuitive, interactive tool for APS exploration. Therefore, we introduce the APS Explorer, a web-based visualization tool for interactive APS exploration, enabling data-driven dataset selection. The APS Explorer provides three interactive features: (1) an interactive PCA plot showing dataset similarity via performance patterns, (2) a dynamic meta-feature table for dataset comparisons, and (3) a specialized visualization for pairwise algorithm performance.