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- W4384643695 abstract "Modern search engines employ multi-stage ranking pipeline to balance retrieval efficiency and effectiveness for large collections. These pipelines retrieve an initial set of candidate documents from the large repository by some cost-effective retrieval model (such as BM25, LM), then re-rank these candidate documents by neural retrieval models. These pipelines perform well if the first-stage ranker achieves high recall [2]. To achieve this, the first-stage ranker should address the problems in milliseconds." @default.
- W4384643695 created "2023-07-19" @default.
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- W4384643695 date "2023-07-18" @default.
- W4384643695 modified "2023-09-26" @default.
- W4384643695 title "Resilient Retrieval Models for Large Collection" @default.
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- W4384643695 doi "https://doi.org/10.1145/3539618.3591793" @default.
- W4384643695 hasPublicationYear "2023" @default.
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