{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:6AR3FESSP2QDDYIFZP4PBK3D7J","short_pith_number":"pith:6AR3FESS","schema_version":"1.0","canonical_sha256":"f023b292527ea031e105cbf8f0ab63fa5f549d96737e41d6ace4b93bde2ffa5a","source":{"kind":"arxiv","id":"2307.15893","version":1},"attestation_state":"computed","paper":{"title":"Online Matching: A Real-time Bandit System for Large-scale Recommendations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Charles Wu, Ed H. Chi, Hariharan Chandrasekaran, Lichan Hong, Lukasz Heldt, Minmin Chen, Ruining He, Shao-Chuan Wang, Xinyang Yi","submitted_at":"2023-07-29T05:46:27Z","abstract_excerpt":"The last decade has witnessed many successes of deep learning-based models for industry-scale recommender systems. These models are typically trained offline in a batch manner. While being effective in capturing users' past interactions with recommendation platforms, batch learning suffers from long model-update latency and is vulnerable to system biases, making it hard to adapt to distribution shift and explore new items or user interests. Although online learning-based approaches (e.g., multi-armed bandits) have demonstrated promising theoretical results in tackling these challenges, their p"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2307.15893","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-29T05:46:27Z","cross_cats_sorted":[],"title_canon_sha256":"4f0a6bba5a0fc9027ae8e0edb2f6eb35562dddc1479659cef901542510a6c9f3","abstract_canon_sha256":"173221df7511daa0bb966c0a1ff0672c90e7404a2a38031374bb589d39aaebcb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:35:55.897824Z","signature_b64":"yU5lb6cFslx5AD8Fsci0GmqQfbyiH7gL3OTjEfH4eDZnYPPdjiKvv8QISQ8h+qawStDk9m8KsDlrdUS60yv/Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f023b292527ea031e105cbf8f0ab63fa5f549d96737e41d6ace4b93bde2ffa5a","last_reissued_at":"2026-07-05T06:35:55.897411Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:35:55.897411Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Online Matching: A Real-time Bandit System for Large-scale Recommendations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Charles Wu, Ed H. Chi, Hariharan Chandrasekaran, Lichan Hong, Lukasz Heldt, Minmin Chen, Ruining He, Shao-Chuan Wang, Xinyang Yi","submitted_at":"2023-07-29T05:46:27Z","abstract_excerpt":"The last decade has witnessed many successes of deep learning-based models for industry-scale recommender systems. These models are typically trained offline in a batch manner. While being effective in capturing users' past interactions with recommendation platforms, batch learning suffers from long model-update latency and is vulnerable to system biases, making it hard to adapt to distribution shift and explore new items or user interests. Although online learning-based approaches (e.g., multi-armed bandits) have demonstrated promising theoretical results in tackling these challenges, their p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.15893","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/2307.15893/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2307.15893","created_at":"2026-07-05T06:35:55.897475+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.15893v1","created_at":"2026-07-05T06:35:55.897475+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.15893","created_at":"2026-07-05T06:35:55.897475+00:00"},{"alias_kind":"pith_short_12","alias_value":"6AR3FESSP2QD","created_at":"2026-07-05T06:35:55.897475+00:00"},{"alias_kind":"pith_short_16","alias_value":"6AR3FESSP2QDDYIF","created_at":"2026-07-05T06:35:55.897475+00:00"},{"alias_kind":"pith_short_8","alias_value":"6AR3FESS","created_at":"2026-07-05T06:35:55.897475+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6AR3FESSP2QDDYIFZP4PBK3D7J","json":"https://pith.science/pith/6AR3FESSP2QDDYIFZP4PBK3D7J.json","graph_json":"https://pith.science/api/pith-number/6AR3FESSP2QDDYIFZP4PBK3D7J/graph.json","events_json":"https://pith.science/api/pith-number/6AR3FESSP2QDDYIFZP4PBK3D7J/events.json","paper":"https://pith.science/paper/6AR3FESS"},"agent_actions":{"view_html":"https://pith.science/pith/6AR3FESSP2QDDYIFZP4PBK3D7J","download_json":"https://pith.science/pith/6AR3FESSP2QDDYIFZP4PBK3D7J.json","view_paper":"https://pith.science/paper/6AR3FESS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.15893&json=true","fetch_graph":"https://pith.science/api/pith-number/6AR3FESSP2QDDYIFZP4PBK3D7J/graph.json","fetch_events":"https://pith.science/api/pith-number/6AR3FESSP2QDDYIFZP4PBK3D7J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6AR3FESSP2QDDYIFZP4PBK3D7J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6AR3FESSP2QDDYIFZP4PBK3D7J/action/storage_attestation","attest_author":"https://pith.science/pith/6AR3FESSP2QDDYIFZP4PBK3D7J/action/author_attestation","sign_citation":"https://pith.science/pith/6AR3FESSP2QDDYIFZP4PBK3D7J/action/citation_signature","submit_replication":"https://pith.science/pith/6AR3FESSP2QDDYIFZP4PBK3D7J/action/replication_record"}},"created_at":"2026-07-05T06:35:55.897475+00:00","updated_at":"2026-07-05T06:35:55.897475+00:00"}