Detecting near-duplicates for web crawling
Abstract
Near-duplicate web documents are abundant. Two such documents differ from each other in a very small portion that displays advertisements, for example. Such differences are irrelevant for web search. So the quality of a web crawler increases if it can assess whether a newly crawled web page is a near-duplicate of a previously crawled web page or not. In the course of developing a near-duplicate detection system for a multi-billion page repository, we make two research contributions. First, we demonstrate that Charikar's fingerprinting technique is appropriate for this goal. Second, we present an algorithmic technique for identifying existing f-bit fingerprints that differ from a given fingerprint in at most k bit-positions, for small k. Our technique is useful for both online queries (single fingerprints) and all batch queries (multiple fingerprints). Experimental evaluation over real data confirms the practicality of our design.
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By collapsing near-duplicates into single representatives, the repository reduces retrieval noise and strengthens utility signals.
“By collapsing near-duplicates into single representatives, the repository reduces retrieval noise and strengthens utility signals [1, 32, 4, 64].”
From SEDM: Scalable Self-Evolving Distributed Memory for Agents (2025) · cites this paper as [32]
Cites
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004 · see citing works
Schleimer, S., S, W. D., & Aiken, A. (2003) · Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data · doi:10.1145/872757.872770 · Cited by 1,099 · cited 1× · see citing works
“This problem has been ameliorated by the “winnowing” technique by Schliemer et al [50].”
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The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
Laurençon, H., Saulnier, L., Wang, T., Akiki, C., del Moral, A. V., Scao, T. L., Von Werra, L., Mou, C., Ponferrada, E. G., Nguyen, H., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.03915 · Cited by 61 · see citing works
“To remove near duplicate documents in OSCAR (which is already exact-deduplicated) we initially used SimHash (Charikar, 2002; Manku et al., 2007), a hashing function that associates to two similar texts hashes with a low Hamming distance, with 6-grams and a Hamming distance threshold of 4.”
MethodSEDM: Scalable Self-Evolving Distributed Memory for Agents
Xu, H., Hu, J., Zhang, K., Yu, L., Tang, Y., Song, X., Duan, Y., Ai, L., & Shi, B. (2025) · arXiv · doi:10.48550/arxiv.2509.09498
“By collapsing near-duplicates into single representatives, the repository reduces retrieval noise and strengthens utility signals [1, 26, 4, 51].”
- Authors
- Manku, G. S., Jain, A., & Das Sarma, A.
- Affiliations
- Google (United States) (US)
- Stanford University, Stanford, CA
- ORCID
- Jain, Arvind
- Venue
- Proceedings of the 16th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2007-05-08
- Pages
- 141-150
- OpenAlex
- W2145349611
- Cited by
- 617
- References
- 2
- Citations by year
- 2026: 17 · 2025: 19 · 2024: 18 · 2023: 33 · 2022: 22 · 2021: 38 · 2020: 35 · 2019: 46 · 2018: 39 · 2017: 34 · 2016: 31 · 2015: 37 · 2014: 54 · 2013: 56 · 2012: 33
- FWCI
- 16.47
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Algorithms and Data Compression
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Keywords
- near-duplicate detection, web crawling, locality-sensitive hashing
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