Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies

Forum topic · 小凯 · 2026-06-09

Summary

This arXiv paper (2506.08633) by Ekaterina Grishina, Stepan Kuznetsov, and Askar Tsyganov introduces a data-driven ranking methodology for recommendation algorithms based on the Bradley-Terry (BT) model. Ranking recommenders is difficult because their performance depends heavily on dataset characteristics such as sparsity, sequential structure, and scale, so naive aggregation of metrics like averaged NDCG over benchmarks can produce misleading rankings. The proposed BT-based approach instead produces rankings that depend on key dataset statistics. The authors also propose a novel metric for evaluating ranking consistency and show the rankings remain robust to incomplete data. Additionally, they introduce a dataset-specific methodology that ranks algorithms on unseen datasets without running the models, extending the BT framework with BT trees and BT models with covariates. Published June 11, 2025, the work targets fair, statistically grounded comparison of recommender systems across diverse benchmark datasets.

Overview

Field: Machine Learning
Authors: Ekaterina Grishina, Stepan Kuznetsov, Askar Tsyganov
Published: 2025-06-11
arXiv: 2506.08633

Abstract

The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale. This drives a demand for a proper methodology for fair comparison between algorithms. Naive aggregation of performance metrics (e.g., averaging NDCG over benchmarks) can yield misleading rankings, undermining practical selection. To address this problem, the authors introduce a novel, data-driven ranking methodology based on the Bradley-Terry (BT) model. They demonstrate that the obtained ranking depends on key dataset statistics. Additionally, they propose a novel metric for evaluating ranking consistency and demonstrate the robustness of the ranking to incomplete data. Finally, they introduce a dataset-specific methodology for ranking algorithms on unseen datasets without running the models, relying on extensions of the BT framework, including BT trees and BT models with covariates.

Key Points

Recommender performance is highly sensitive to dataset properties (sparsity, sequential structure, scale), making fair algorithm comparison difficult.
Averaging metrics like NDCG across benchmarks can lead to misleading rankings.
The proposed Bradley-Terry-based ranking depends on key dataset statistics.
A new consistency metric evaluates ranking quality; the ranking is robust to incomplete data.
Extensions (BT trees, BT with covariates) enable ranking algorithms on unseen datasets without running them.

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