2022/02/14 by Yegor Tkachenko, Tkachenko, Yegor, Wassim Dhaouadi +3
Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Multi-Criteria Decision Making #Smart Parking Systems Research #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.2202.07088
12 pages, 2 figures, 3 tables
arxiv created 2022/02/14 · openalex publication_date 2022/02/14 · arxiv updated 2022/02/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many important multiple-objective decision problems can be cast within the framework of ranking under constraints and solved via a weighted bipartite matching linear program. Some of these optimization problems, such as personalized content recommendations, may need to be solved in real time and thus must comply with strict time requirements to prevent the perception of latency by consumers. Classical linear programming is too computationally inefficient for such settings. We propose a novel approach to scale up ranking under constraints by replacing the weighted bipartite matching optimization with a prediction problem in the algorithm deployment stage. We show empirically that the proposed approximate solution to the ranking problem leads to a major reduction in required computing resources without much sacrifice in constraint compliance and achieved utility, allowing us to solve larger constrained ranking problems real-time, within the required 50 milliseconds, than previously reported.