{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:2KY36QLR3FVLUFA5FM3I2ROOUR","short_pith_number":"pith:2KY36QLR","schema_version":"1.0","canonical_sha256":"d2b1bf4171d96aba141d2b368d45cea47c1ac484fda3fed99f85c572202d90a1","source":{"kind":"arxiv","id":"2106.03593","version":2},"attestation_state":"computed","paper":{"title":"Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.GT","authors_text":"Chuan Yu, Dagui Chen, Da Huo, Fan Wu, Guihai Chen, Hongtao Lv, Jian Xu, Xiangyu Liu, Xiaoqiang Zhu, Yiqing Wang, Yu Rong, Zhenzhe Zheng, Zhilin Zhang","submitted_at":"2021-06-07T13:20:40Z","abstract_excerpt":"In e-commerce advertising, it is crucial to jointly consider various performance metrics, e.g., user experience, advertiser utility, and platform revenue. Traditional auction mechanisms, such as GSP and VCG auctions, can be suboptimal due to their fixed allocation rules to optimize a single performance metric (e.g., revenue or social welfare). Recently, data-driven auctions, learned directly from auction outcomes to optimize multiple performance metrics, have attracted increasing research interests. However, the procedure of auction mechanisms involves various discrete calculation operations, "},"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":"2106.03593","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GT","submitted_at":"2021-06-07T13:20:40Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"7da613e3c9db7b4ddbb1cff6cfecae62e0cebe35746025b8a590ce94ff95f517","abstract_canon_sha256":"a721faa74ba5517c6299b4f1f9fa631a577c1dc0123905daba10e254713b8699"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:57:54.798730Z","signature_b64":"RcD0qd4DaPVIIwRfQueUnDDlYA1lawKpyhI95iJ4f1zhXXlkbKTpt4Rh4kgCNqMQMRM4ug1BDu6dtDfrglpLDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2b1bf4171d96aba141d2b368d45cea47c1ac484fda3fed99f85c572202d90a1","last_reissued_at":"2026-07-05T02:57:54.798175Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:57:54.798175Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.GT","authors_text":"Chuan Yu, Dagui Chen, Da Huo, Fan Wu, Guihai Chen, Hongtao Lv, Jian Xu, Xiangyu Liu, Xiaoqiang Zhu, Yiqing Wang, Yu Rong, Zhenzhe Zheng, Zhilin Zhang","submitted_at":"2021-06-07T13:20:40Z","abstract_excerpt":"In e-commerce advertising, it is crucial to jointly consider various performance metrics, e.g., user experience, advertiser utility, and platform revenue. Traditional auction mechanisms, such as GSP and VCG auctions, can be suboptimal due to their fixed allocation rules to optimize a single performance metric (e.g., revenue or social welfare). Recently, data-driven auctions, learned directly from auction outcomes to optimize multiple performance metrics, have attracted increasing research interests. However, the procedure of auction mechanisms involves various discrete calculation operations, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.03593","kind":"arxiv","version":2},"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/2106.03593/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":"2106.03593","created_at":"2026-07-05T02:57:54.798239+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.03593v2","created_at":"2026-07-05T02:57:54.798239+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.03593","created_at":"2026-07-05T02:57:54.798239+00:00"},{"alias_kind":"pith_short_12","alias_value":"2KY36QLR3FVL","created_at":"2026-07-05T02:57:54.798239+00:00"},{"alias_kind":"pith_short_16","alias_value":"2KY36QLR3FVLUFA5","created_at":"2026-07-05T02:57:54.798239+00:00"},{"alias_kind":"pith_short_8","alias_value":"2KY36QLR","created_at":"2026-07-05T02:57:54.798239+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/2KY36QLR3FVLUFA5FM3I2ROOUR","json":"https://pith.science/pith/2KY36QLR3FVLUFA5FM3I2ROOUR.json","graph_json":"https://pith.science/api/pith-number/2KY36QLR3FVLUFA5FM3I2ROOUR/graph.json","events_json":"https://pith.science/api/pith-number/2KY36QLR3FVLUFA5FM3I2ROOUR/events.json","paper":"https://pith.science/paper/2KY36QLR"},"agent_actions":{"view_html":"https://pith.science/pith/2KY36QLR3FVLUFA5FM3I2ROOUR","download_json":"https://pith.science/pith/2KY36QLR3FVLUFA5FM3I2ROOUR.json","view_paper":"https://pith.science/paper/2KY36QLR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.03593&json=true","fetch_graph":"https://pith.science/api/pith-number/2KY36QLR3FVLUFA5FM3I2ROOUR/graph.json","fetch_events":"https://pith.science/api/pith-number/2KY36QLR3FVLUFA5FM3I2ROOUR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2KY36QLR3FVLUFA5FM3I2ROOUR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2KY36QLR3FVLUFA5FM3I2ROOUR/action/storage_attestation","attest_author":"https://pith.science/pith/2KY36QLR3FVLUFA5FM3I2ROOUR/action/author_attestation","sign_citation":"https://pith.science/pith/2KY36QLR3FVLUFA5FM3I2ROOUR/action/citation_signature","submit_replication":"https://pith.science/pith/2KY36QLR3FVLUFA5FM3I2ROOUR/action/replication_record"}},"created_at":"2026-07-05T02:57:54.798239+00:00","updated_at":"2026-07-05T02:57:54.798239+00:00"}