{"paper":{"title":"Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.DB","cs.DS","cs.PF"],"primary_cat":"cs.LG","authors_text":"Artem Babenko, Dmitry Baranchuk, George Williams, Gopal Srinivasa, Harsha Vardhan Simhadri, Jingdong Wang, Lucas Hosseini, Martin Aum\\\"uller, Matthijs Douze, Qi Chen, Ravishankar Krishnaswamy, Suhas Jayaram Subramanya","submitted_at":"2022-05-08T02:41:54Z","abstract_excerpt":"Despite the broad range of algorithms for Approximate Nearest Neighbor Search, most empirical evaluations of algorithms have focused on smaller datasets, typically of 1 million points~\\citep{Benchmark}. However, deploying recent advances in embedding based techniques for search, recommendation and ranking at scale require ANNS indices at billion, trillion or larger scale. Barring a few recent papers, there is limited consensus on which algorithms are effective at this scale vis-\\`a-vis their hardware cost.\n  This competition compares ANNS algorithms at billion-scale by hardware cost, accuracy "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.03763","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2205.03763/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}