推荐系统团队做算法选型时，别再被平均指标骗了——BT模型帮你根据数据集特性精准排名，省去跑全量模型的成本，做评估基准的开发者值得一试。
推荐算法排名因数据集特性（稀疏性、序列结构、规模）而异，简单平均指标（如NDCG）会产生误导。研究者提出基于Bradley-Terry模型的排名方法，能根据数据集统计特征生成更可靠的排名。该方法还引入新指标评估排名一致性，并能在不运行模型的情况下预测新数据集上的算法表现。这对推荐系统开发者选择算法和评估基准有重要参考价值。
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 robustness of our ranking to incomplete data. Finally, we introduce a dataset-specific methodology for ranking algorithms on unseen datasets without running the models, relying on extensions of the Bradley-Terry framework, including BT trees and BT models with covariates.