Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies
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, we introduce a novel, data-driven ranking methodology based on Bradley-Terry (BT) model. We demonstrate that the obtained ranking depends on key dataset statistics. Additionally, we propose a novel metric for evaluating ranking consistency and demonstrate robus...
| Authors | Ekaterina Grishina, Stepan Kuznetsov, Askar Tsyganov et al. |
|---|---|
| Primary category | cs.IR |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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