APS Explorer: Navigating Algorithm Performance Spaces for Informed Dataset Selection

2025-08-26

AI Summary

In offline recommendation experiments, dataset selection often lacks principled justification, relying excessively on a few dense benchmarks—leading to unreliable conclusions in sparse or cold-start scenarios. Although the Algorithm Performance Space (APS) framework theoretically enables systematic dataset–algorithm alignment, its practical adoption is hindered by the absence of intuitive, interactive visualization tools. To bridge this gap, we propose APS Explorer—the first interactive web-based tool for visualizing the APS in recommender systems. It integrates three core components: (i) a PCA-reduced similarity graph of datasets, (ii) a dynamic meta-feature table, and (iii) pairwise algorithm performance heatmaps. Evaluated across mainstream recommendation datasets, APS Explorer enables effective identification of datasets aligned with specific experimental requirements (e.g., sparsity, cold-start robustness), thereby enhancing reproducibility and reliability of offline evaluation.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Search and Optimization: Algorithm Configuration
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Lack of justification for dataset selection in recommender systems
• Limited adoption of Algorithm Performance Spaces due to missing tools
• Need for interactive visualization to enable data-driven dataset choices

Innovation

• Web-based interactive visualization tool
• Interactive PCA plot for dataset similarity
• Dynamic meta-feature table for comparisons