GoodTP: An Effective Data Selection Framework for Enhancing Trajectory Similarity Learning via Monte Carlo Tree Search
Summary: GoodTP replaces costly labeled-pair selection with iterative trajectory selection over a hierarchical clustering tree. MCTS with UCB-guided exploration and cache-enhanced bilevel weighting learns globally effective training sets, improving neural trajectory-similarity models. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Haitao Yuan (Nanyang Technological University)
- 2. Gao Cong (Nanyang Technological University)
BibTeX Citation
@inproceedings{yuan_sigmod26,
title = {{GoodTP: An Effective Data Selection Framework for Enhancing Trajectory Similarity Learning via Monte Carlo Tree Search}},
author = {Yuan, Haitao and Cong, Gao},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802067},
url = {https://dl.acm.org/doi/10.1145/3802067},
year = {2026}
}
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