Active Learning for Matching Problems

2012/06/18 by Laurent Charlin, Charlin, Laurent, Craig Boutilier +2
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning and Algorithms #Mobile Crowdsensing and Crowdsourcing #Optimization and Search Problems

paper · pdf · doi:10.48550/arxiv.1206.4647

openalex publication_date 2012/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Effective learning of user preferences is critical to easing user burden in various types of matching problems. Equally important is active query selection to further reduce the amount of preference information users must provide. We address the problem of active learning of user preferences for matching problems, introducing a novel method for determining probabilistic matchings, and developing several new active learning strategies that are sensitive to the specific matching objective. Experiments with real-world data sets spanning diverse domains demonstrate that matching-sensitive active learning

Citations

• A Framework for Optimizing Paper Matching
• College Admissions and the Stability of Marriage
• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

Related

• Learning Active Learning from Data
• Active metric learning and classification using similarity queries
• ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment
• Active Learning in Recommendation Systems with Multi-level User Preferences
• Active Algorithms For Preference Learning Problems with Multiple Populations