rkHit: Representative Query with Uncertain Preference
Summary: Proposes rkHit, selecting r representative tuples under a distribution over scoring functions to maximize the probability a chosen tuple is attractive in top-k (expected user satisfaction). In 2D, 2DH gives an exact polynomial algorithm; rkHit is NP-hard for d≥3; 3D offers a (1−1/e)-approximation 3DH under uniform preferences while MDH handles any dimension/distribution via sampling+clustering; experiments show efficiency. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Xingxing Xiao (Harbin Engineering University; Shenzhen University)
- 2. Jianzhong Li (Shenzhen University)
BibTeX Citation
@inproceedings{xiao_sigmod23,
title = {{rkHit: Representative Query with Uncertain Preference}},
author = {Xiao, Xingxing and Li, Jianzhong},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3589271},
url = {https://dl.acm.org/doi/10.1145/3589271},
year = {2023}
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