AI 中文总结
本文针对排序模型统一序列建模与特征交互时面临的问题，提出UniRank基准，介绍其方法及工具包，通过在五个大规模公共数据集上对15个模型测试，为相关研究提供可重复基础，缩小研究差距，使多方受益。
AI 中文摘要
排序是在线广告和推荐系统的核心阶段。现代排序模型越来越多地将序列建模和特征交互统一起来，但许多进展依赖专有数据、封闭实现和大规模工业基础设施。这限制了可重复比较，阻碍了对缩放定律、长序列建模和多任务排序的学术研究。为解决这些限制，本文提出UniRank，一个用于统一序列建模和特征交互的排序模型开放基准。UniRank使用按时间顺序的逐点自回归监督，标准化跨反馈任务的评估，并提供具有分布式数据并行训练、算子优化、混合精度训练、注意力优化等效率技术的PyTorch工具包，降低硬件需求。我们在来自短视频、广告和电子商务平台的五个大规模公共数据集上对15个代表性统一排序模型进行基准测试，最大数据集包含超过7亿个实例，最长行为序列超过10^5次交互。UniRank为比较统一排序模型、研究有限计算下的缩放定律以及缩小学术和工业排序研究差距提供了可重复的基础。我们相信UniRank通过可重复实验、面向生产的评估和可访问的实现，使研究人员、从业者和初学者受益。代码和数据可在该https网址获取。
英文摘要
Ranking is a core stage in online advertising and recommender systems. Modern ranking models increasingly unify sequential modeling and feature interaction, yet many advances rely on proprietary data, closed implementations, and large-scale industrial infrastructure. This setting limits reproducible comparison and hinders academic study of scaling laws, long-sequence modeling, and multi-task ranking. To address these limitations, this paper proposes UniRank, an open benchmark for ranking models that unify sequential modeling and feature interaction. UniRank uses chronological pointwise autoregressive supervision, standardizes evaluation across feedback tasks, and provides a PyTorch toolkit with Distributed Data Parallel training, operator optimization, mixed-precision training, attention optimization, and other efficiency techniques that reduce hardware requirements. We benchmark 15 representative unified ranking models on five large-scale public datasets from short-video, advertising, and e-commerce platforms, with the largest dataset containing over 700 million instances and the longest behavior sequence exceeding 10^5 interactions. UniRank provides a reproducible basis for comparing unified ranking models, studying scaling laws under limited compute, and narrowing the gap between academic and industrial ranking research. We believe UniRank benefits researchers, practitioners, and beginners through reproducible experiments, production-oriented evaluation, and accessible implementations. Code and data are available at https://github.com/salmon1802/UniRank.
Comments11 pages, 6 figures, and 7 tables. Code and data: https://github.com/salmon1802/UniRank
