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CRAFT: Corpus Relatedness Analysis Using Fourier Transforms

Summary: CRAFT sketches term co-occurrence signals with randomized Fourier transforms, avoiding materialization of the term-document matrix. Compressed sensing and Orthogonal Matching Pursuit then recover sparse, precise relatedness without quadratic all-pairs comparisons, improving scalability and precision. (summarized by gpt-5.6-luna on Aug 28 2026)

Paper ID
h690d70c62f4af346
Venue
VLDB
Year
2026
Pagerank
4.9793485e-05
Overall Rank
10,815 | 27.29%
DOI
10.14778/3828612.3828622

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BibTeX Citation

@article{chen_vldb26,
        title = {{CRAFT: Corpus Relatedness Analysis Using Fourier Transforms}},
        author = {Chen, Kaiwen and Koudas, Nick},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {10},
        pages = {2658--2671},
        doi = {10.14778/3828612.3828622},
        url = {https://doi.org/10.14778/3828612.3828622},
        year = {2026}
}

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