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META: An Efficient Matching-Based Method for Error-Tolerant Autocompletion

Summary: Meta proposes a matching-based framework for error-tolerant autocompletion, replacing trie-centric active-node computation with character-level matching. It introduces a compact tree index to maintain active nodes, an incremental top-k algorithm, and achieves 1–2 orders of magnitude speedups over state-of-the-art on real datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11370
Venue
VLDB
Year
2016
Pagerank
4.3254416e-05
Overall Rank
9,567 | 33.45%
DOI
-

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