用强化学习训练的Transformer模型，高效逼近最优排名聚合结果。
Towards Foundation Models for Consensus Rank Aggregation
- 基于Transformer与强化学习，自动学习最优排名聚合策略。
- 在多个数据集上优于传统方法，推理速度远超整数规划求解器。
- 适合大规模实际场景中的排名融合，如推荐系统与选举分析。
从多个输入排序中聚合共识排序是一个基础问题，广泛应用于推荐系统、搜索引擎、招聘和选举等领域。尽管多年来研究不断，最小化Kemeny距离仍因计算不可行而受限：确定相对于Kemeny距离的最优排序聚合是NP-hard问题，限制了其在中小规模实例外的实际应用。本文提出Kemeny Transformer，一种基于Transformer的算法，通过强化学习训练以高效近似Kemeny最优排序。实验表明，该模型优于经典多数启发式和马尔可夫链方法，在多个数据集上表现更优，且推理速度显著快于整数线性规划求解器。因此，本方法为真实世界的排序聚合任务提供了一种实用且可扩展的替代方案。
原文摘要 · Abstract (English)
Aggregating a consensus ranking from multiple input rankings is a fundamental problem with applications in recommendation systems, search engines, job recruitment, and elections. Despite decades of research in consensus ranking aggregation, minimizing the Kemeny distance remains computationally intractable. Specifically, determining an optimal aggregation of rankings with respect to the Kemeny distance is an NP-hard problem, limiting its practical application to relatively small-scale instances. We propose the Kemeny Transformer, a novel Transformer-based algorithm trained via reinforcement learning to efficiently approximate the Kemeny optimal ranking. Experimental results demonstrate that our model outperforms classical majority-heuristic and Markov-chain approaches, achieving substantially faster inference than integer linear programming solvers. Our approach thus offers a practical, scalable alternative for real-world ranking-aggregation tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。