APS Explorer: Data-Driven Dataset Selection

• APS Explorer is a web-based visualization tool that employs algorithm performance spaces to facilitate informed dataset selection for recommender system experiments.
• It integrates interactive PCA plots, dynamic meta-feature tables, and pairwise algorithm performance grids to analyze dataset similarity and difficulty.
• By addressing unjustified dataset choices and promoting transparency, the tool enhances experimental rigor and reduces risks of overfitting in benchmarking.

APS Explorer is a web-based visualization tool designed to facilitate informed and data-driven dataset selection for offline recommender system experiments. It leverages the concept of Algorithm Performance Spaces (APS), representing each dataset as a point in a multidimensional space defined by the performance of 29 different recommendation algorithms. The tool addresses the pervasive issue in the recommender systems community where most experimental papers rely on a small set of popular datasets without rigorous justification, often leading to unreliable or non-generalizable results due to mismatched data properties. APS Explorer provides researchers with interactive features for exploring dataset similarity, comparing meta-features, and analyzing pairwise algorithm performance, thereby supporting the technical rigor and transparency in experimental design (Vente et al., 26 Aug 2025).

1. Motivation and Conceptual Foundation

The impetus for APS Explorer is rooted in the recognized necessity for justified dataset selection in recommender systems research. Empirical results are highly sensitive to the underlying dataset properties—with mismatches such as using dense datasets for sparse interaction scenarios exacerbating overfitting and biasing conclusions. According to the paper, 86% of ACM RecSys 2024 papers offered no justification for their dataset choices, instead defaulting to a narrow set (Amazon, MovieLens, Yelp, Gowalla accounting for a combined 99%). The Algorithm Performance Spaces (APS) concept was previously proposed to systematically guide dataset selection; however, its practical adoption has been hampered by the lack of an intuitive tool for APS exploration.

APS Explorer operationalizes APS by quantifying dataset similarity and "difficulty" via the collective performance of a broad suite of algorithms. Each dataset is embedded as a vector in 29-dimensional space (one dimension per algorithm), enabling nuanced analysis beyond conventional meta-features.

2. Core Interactive Features

APS Explorer integrates three principal modules to support dataset evaluation:

(a) Interactive PCA Plot

The high-dimensional APS, where each axis corresponds to an algorithm’s performance metric (such as nDCG, Recall, or Hit Ratio), is projected into two dimensions via Principal Component Analysis (PCA). This visualization enables users to observe spatial relationships among datasets:

• Datasets that cluster closely exhibit similar performance profiles across the 29 algorithms.
• Users can interactively filter datasets or select subsets of algorithms, highlighting patterns of similarity or outliers that may warrant closer scrutiny.

(b) Dynamic Meta-Feature Table

Datasets are further characterized by a suite of metadata (sparsity ratio, user-item interaction distributions, cold-start risk indicators, etc.) presented in a sortable and filterable table.

• This module permits systematic side-by-side comparison of dataset properties, augmenting the algorithmic view with concrete descriptors (e.g., quantifying sparsity for sparse/dense domain tasks).
• Export and selection capabilities enable researchers to curate datasets tailored to specific experimental requirements.

(c) Specialized Pairwise Algorithm Performance Visualization

Researchers can compare two algorithms' performance across datasets using quadrant-based partitioning:

• The upper-right quadrant (green) marks datasets where both algorithms achieve top 25% performance (joint excellence), while the lower-left quadrant (red) indicates simultaneous underperformance.
• Blue and yellow regions denote asymmetric dominance, where one algorithm consistently outperforms the other.
• Researchers can leverage these visual cues to identify datasets that are particularly challenging or expose algorithmic strengths/weaknesses.

Feature	Description	Example Visualization
PCA Plot	Maps dataset similarity via algorithmic performance	2D projection in APS space
Meta-Feature Table	Tabular comparison of intrinsic dataset properties	Sparsity, distribution stats
Pairwise Performance Grid	Quadrant-based comparison for two algorithms	Color-coded quadrants

3. Technical Implementation

The construction of APS Explorer involved a systematic evaluation of 29 recommendation algorithms on 96 distinct datasets, covering multiple performance metrics (nDCG, Recall, Hit Ratio) and enabling various $K$-value parameter settings for flexible analysis.

• The APS vectors are reduced for visualization via PCA, which preserves the principal differences in performance profiles.
• A "difficulty score" is computed for each dataset using normalized performance components:

$$\text{Difficulty Score} = (\text{norm}(C_1) + \text{norm}(C_2))/2$$

where norm($C_1$) and norm($C_2$) are normalized summary statistics, yielding a quintile-based categorization into five levels of difficulty.

• Percentile thresholding in the pairwise algorithm module ensures statistically balanced quadrant visualization (top 25%, bottom 25%, etc.).
• The modular, extensible, and web-based architecture is designed for real-time interactivity, including metric selection and data export (such as CSV file generation for further analysis).

4. Impact on Research Practices

APS Explorer directly addresses concerns about reproducibility and methodological rigor in recommender system research. By allowing empirical justification for dataset selection:

• It reduces the risk of overfitting to a small selection of popular benchmarks.
• Researchers are empowered to match datasets to their intended experimental tasks (e.g., preferably using low-density data for sparse user-item interaction modeling).
• Transparent visualization of performance patterns and dataset meta-features fosters more deliberate and scientifically robust benchmarking.

The paper reports early adoption facilitated by the public release of the source code and web interface, which enables in-depth analysis of large-scale algorithm/dataset performance data.

5. Limitations and Future Directions

APS Explorer is positioned as an extensible tool, with ongoing development planned to meet evolving needs:

• The system may be broadened to include compatibility with additional performance metrics, dataset descriptors, and enhanced customization of visualizations.
• Integration with direct export capabilities (CSV) and streamlined user workflows is envisaged to increase utility.
• A plausible implication is that, as the recommender systems community demands greater rigor in dataset selection, APS Explorer could evolve to support real-time updates with new datasets or algorithmic results, further aiding benchmarking and research transparency.

6. Contextual Significance

Dataset selection remains a fundamental, often under-justified aspect of recommender system experimentation. APS Explorer embodies a systematic approach to this challenge, merging advanced algorithmic benchmarking with comprehensive metadata comparison. This suggests a shift toward more deliberate, data-driven experiment design in recommender system research, emphasizing justification and transparency in evaluating algorithmic performance across diverse domains.

7. Summary

APS Explorer is a web-based tool for interactive visualization and analysis within Algorithm Performance Spaces, supporting informed dataset selection in recommender systems experimentation (Vente et al., 26 Aug 2025). By providing PCA-based similarity mapping, meta-feature tables, and pairwise algorithm comparison visualizations, it enables researchers to match datasets to experimental needs based on empirical and metadata-driven justification. This framework advances best practices in experimental methodology by addressing reproducibility and transparency concerns, illustrating a pragmatic shift in recommender systems research toward data-driven justification in benchmarking.