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Latent Semantic Indexing: A Probabilistic Analysis

Summary: Gives a rigorous probabilistic analysis proving LSI recovers latent semantics and improves retrieval under specific generative conditions. Proposes random-projection acceleration and frames results as theoretical justification for spectral methods (e.g., collaborative filtering). (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1140
Venue
PODS
Year
1998
Pagerank
0.00012097985
Overall Rank
1,119 | 92.33%
DOI
10.1145/275487.275505

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{papadimitriou_pods98,
        address = {New York, NY, USA},
        series = {{PODS} '98},
        title = {{Latent Semantic Indexing: A Probabilistic Analysis}},
        url = {https://dl.acm.org/doi/10.1145/275487.275505},
        doi = {10.1145/275487.275505},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Papadimitriou, Christos H. and Raghavan, Prabhakar and Tamaki, Hisao and Vempala, Santosh},
        year = {1998}
}

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Outgoing Citations (Sorted by Pagerank)

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
170 Combining Fuzzy Information from Multiple Systems 1996 PODS 0.00027376361
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