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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)

Paper ID
7440
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,249 | 29.69%
DOI
10.1145/3802067

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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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