{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:JDGHSZZO5LCEONL4NDOV7OSDXX","short_pith_number":"pith:JDGHSZZO","schema_version":"1.0","canonical_sha256":"48cc79672eeac447357c68dd5fba43bdff123bba02e75cf26b1ce0ec5a12a00f","source":{"kind":"arxiv","id":"2302.06141","version":1},"attestation_state":"computed","paper":{"title":"DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Chaochao Chen, Fei Wu, Feng Zhu, Guanfeng Liu, Jun Zhou, Longfei Li, Lu Yu, Mingjie Zhong, Tiehua Zhang, Xinxing Yang, Yan Wang","submitted_at":"2023-02-13T07:03:46Z","abstract_excerpt":"In recommendation scenarios, there are two long-standing challenges, i.e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-Through Rate (CTR) and post-click Conversion Rate (CVR) tasks. To cope with these issues, existing works emphasize on leveraging Multi-Task Learning (MTL) frameworks (Category 1) or causal debiasing frameworks (Category 2) to incorporate more auxiliary data in the entire exposure/inference space D or debias the selection bias in the click/training space O. However, these two kinds of solutions cannot effectively addr"},"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":"2302.06141","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2023-02-13T07:03:46Z","cross_cats_sorted":[],"title_canon_sha256":"f68282659a0054522f0aec8bb7b8b5a6ffd9a081b290fde984b7b3e6d1deb880","abstract_canon_sha256":"32691167091db6bc6c64b297acc971ed94293f1bab385684a9fe099cebd11a0e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:41:02.400629Z","signature_b64":"7qlHYJ0jZsc/gSvH5X4dnYALk+nHCEkO+yu7UPM26OIPNqbjNS6Zec3h6tJk1b/G9gcnkfT0sihh2AY+6q0PAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48cc79672eeac447357c68dd5fba43bdff123bba02e75cf26b1ce0ec5a12a00f","last_reissued_at":"2026-07-05T05:41:02.400275Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:41:02.400275Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Chaochao Chen, Fei Wu, Feng Zhu, Guanfeng Liu, Jun Zhou, Longfei Li, Lu Yu, Mingjie Zhong, Tiehua Zhang, Xinxing Yang, Yan Wang","submitted_at":"2023-02-13T07:03:46Z","abstract_excerpt":"In recommendation scenarios, there are two long-standing challenges, i.e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-Through Rate (CTR) and post-click Conversion Rate (CVR) tasks. To cope with these issues, existing works emphasize on leveraging Multi-Task Learning (MTL) frameworks (Category 1) or causal debiasing frameworks (Category 2) to incorporate more auxiliary data in the entire exposure/inference space D or debias the selection bias in the click/training space O. However, these two kinds of solutions cannot effectively addr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.06141","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/2302.06141/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":"2302.06141","created_at":"2026-07-05T05:41:02.400338+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.06141v1","created_at":"2026-07-05T05:41:02.400338+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.06141","created_at":"2026-07-05T05:41:02.400338+00:00"},{"alias_kind":"pith_short_12","alias_value":"JDGHSZZO5LCE","created_at":"2026-07-05T05:41:02.400338+00:00"},{"alias_kind":"pith_short_16","alias_value":"JDGHSZZO5LCEONL4","created_at":"2026-07-05T05:41:02.400338+00:00"},{"alias_kind":"pith_short_8","alias_value":"JDGHSZZO","created_at":"2026-07-05T05:41:02.400338+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/JDGHSZZO5LCEONL4NDOV7OSDXX","json":"https://pith.science/pith/JDGHSZZO5LCEONL4NDOV7OSDXX.json","graph_json":"https://pith.science/api/pith-number/JDGHSZZO5LCEONL4NDOV7OSDXX/graph.json","events_json":"https://pith.science/api/pith-number/JDGHSZZO5LCEONL4NDOV7OSDXX/events.json","paper":"https://pith.science/paper/JDGHSZZO"},"agent_actions":{"view_html":"https://pith.science/pith/JDGHSZZO5LCEONL4NDOV7OSDXX","download_json":"https://pith.science/pith/JDGHSZZO5LCEONL4NDOV7OSDXX.json","view_paper":"https://pith.science/paper/JDGHSZZO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.06141&json=true","fetch_graph":"https://pith.science/api/pith-number/JDGHSZZO5LCEONL4NDOV7OSDXX/graph.json","fetch_events":"https://pith.science/api/pith-number/JDGHSZZO5LCEONL4NDOV7OSDXX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JDGHSZZO5LCEONL4NDOV7OSDXX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JDGHSZZO5LCEONL4NDOV7OSDXX/action/storage_attestation","attest_author":"https://pith.science/pith/JDGHSZZO5LCEONL4NDOV7OSDXX/action/author_attestation","sign_citation":"https://pith.science/pith/JDGHSZZO5LCEONL4NDOV7OSDXX/action/citation_signature","submit_replication":"https://pith.science/pith/JDGHSZZO5LCEONL4NDOV7OSDXX/action/replication_record"}},"created_at":"2026-07-05T05:41:02.400338+00:00","updated_at":"2026-07-05T05:41:02.400338+00:00"}