Active Learning for Matching Problems
This work addresses reducing user burden in matching problems, but it appears incremental as it builds on existing active learning and matching frameworks.
The paper tackles the problem of active learning for matching problems by introducing a novel method for probabilistic matchings and new active learning strategies, demonstrating effectiveness with real-world datasets across diverse domains.
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