2020/06/09 by Jiaqi Ma, Ma, Jiaqi, Xinyang Yi +11
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Game Theory and Voting Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Multi-Criteria Decision Making #Text and Document Classification Technologies
paper · pdf · doi:10.48550/arxiv.2006.05067
openalex publication_date 2020/06/09 · openalex created_date 2020/06/19 · openalex updated_date 2026/07/28
We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint partitions, but the ranking of items within a partition is unknown. Given N items with M partitions, calculating the likelihood of data with partitioned preference under the PL model has a time complexity of O(N+S!), where S is the maximum size of the top M-1 partitions. This computational challenge restrains most existing PL-based listwise LTR methods to a special case of partitioned preference, top-K ranking, where the exact order of the top K items is known. In this paper, we exploit a random utility model formulation of the PL model, and propose an efficient numerical integration approach for calculating the likelihood and its gradients with a time complexity O(N+S3). We demonstrate that the proposed method outperforms well-known LTR baselines and remains scalable through both simulation experiments and applications to real-world eXtreme Multi-Label classification tasks.