Search papers, labs, and topics across Lattice.
This paper introduces UniRank, an open benchmark designed for evaluating ranking models that integrate sequential modeling and feature interaction, addressing the challenges posed by proprietary data and closed implementations in the field. By employing chronological pointwise autoregressive supervision and standardizing evaluations across various feedback tasks, UniRank enables reproducible comparisons among 15 unified ranking models across five large-scale public datasets. The findings reveal that UniRank not only facilitates academic research into scaling laws and long-sequence modeling but also bridges the gap between academic and industrial practices in ranking systems.
UniRank reveals the potential for reproducible benchmarking in ranking models, leveling the playing field between academic research and industrial applications.
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.
