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Weighted Minwise Hashing Beats Linear Sketching for Inner Product Estimation

Summary: Weighted MinHash-based compact sketches for independent pairwise inner-product estimation, provably matching linear sketches on dense vectors and improving error bounds for sparse vectors with limited support overlap. Empirically outperforms CountSketch/JL and unweighted hashing, making it attractive for dataset-search and column-wise covariance/conditional-mean estimation. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1930
Venue
PODS
Year
2023
Pagerank
5.093636e-05
Overall Rank
11,374 | 21.97%
DOI
10.1145/3584372.3588679

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Authors

BibTeX Citation

@inproceedings{bessa_pods23,
        address = {New York, NY, USA},
        series = {{PODS} '23},
        title = {{Weighted Minwise Hashing Beats Linear Sketching for Inner Product Estimation}},
        url = {https://dl.acm.org/doi/10.1145/3584372.3588679},
        doi = {10.1145/3584372.3588679},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Bessa, Aline and Daliri, Majid and Freire, Juliana and Musco, Cameron and Musco, Christopher and Santos, Aécio and Zhang, Haoxiang},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
8,252 Sampling Methods for Inner Product Sketching 2024 VLDB 5.4574671e-05
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