{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:K25ZKXRMIXYJLIJVIFTJ4BWS5R","short_pith_number":"pith:K25ZKXRM","schema_version":"1.0","canonical_sha256":"56bb955e2c45f095a13541669e06d2ec614306b33b0196ef55c069275fcc3553","source":{"kind":"arxiv","id":"1910.09337","version":2},"attestation_state":"computed","paper":{"title":"Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Hong Wen, Keping Yang, Quan Lin, Ramin Ramezani, Wenhao Zhang, Wentian Bao, Xiao-Yang Liu","submitted_at":"2019-10-16T17:46:11Z","abstract_excerpt":"Post-click conversion rate (CVR) estimation is a critical task in e-commerce recommender systems. This task is deemed quite challenging under the industrial setting with two major issues: 1) selection bias caused by user self-selection, and 2) data sparsity due to the rare click events. A successful conversion typically has the following sequential events: \"exposure -> click -> conversion\". Conventional CVR estimators are trained in the click space, but the inference is done in the entire exposure space. They fail to account for the causes of the missing data and treat them as missing at rando"},"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":"1910.09337","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-10-16T17:46:11Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"116bde07fb3955047168362e8be59c431c8d0c2e977616805c5ea02a68b9ae46","abstract_canon_sha256":"3e7f9669d247d7bf48cb90449fa5b4a7d9988903e64bd35245b3f56a7fb71c34"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:52:36.568181Z","signature_b64":"AnG4eOOf4YRRLb2pFyfIWPr1WltXiYQ6618eB0NztfcbZTfck/x/VISwnMKgCKzZtqSm0Abh5cAVCLX4wdiuAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"56bb955e2c45f095a13541669e06d2ec614306b33b0196ef55c069275fcc3553","last_reissued_at":"2026-07-05T00:52:36.567826Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:52:36.567826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Hong Wen, Keping Yang, Quan Lin, Ramin Ramezani, Wenhao Zhang, Wentian Bao, Xiao-Yang Liu","submitted_at":"2019-10-16T17:46:11Z","abstract_excerpt":"Post-click conversion rate (CVR) estimation is a critical task in e-commerce recommender systems. This task is deemed quite challenging under the industrial setting with two major issues: 1) selection bias caused by user self-selection, and 2) data sparsity due to the rare click events. A successful conversion typically has the following sequential events: \"exposure -> click -> conversion\". Conventional CVR estimators are trained in the click space, but the inference is done in the entire exposure space. They fail to account for the causes of the missing data and treat them as missing at rando"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.09337","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/1910.09337/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":"1910.09337","created_at":"2026-07-05T00:52:36.567881+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.09337v2","created_at":"2026-07-05T00:52:36.567881+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.09337","created_at":"2026-07-05T00:52:36.567881+00:00"},{"alias_kind":"pith_short_12","alias_value":"K25ZKXRMIXYJ","created_at":"2026-07-05T00:52:36.567881+00:00"},{"alias_kind":"pith_short_16","alias_value":"K25ZKXRMIXYJLIJV","created_at":"2026-07-05T00:52:36.567881+00:00"},{"alias_kind":"pith_short_8","alias_value":"K25ZKXRM","created_at":"2026-07-05T00:52:36.567881+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/K25ZKXRMIXYJLIJVIFTJ4BWS5R","json":"https://pith.science/pith/K25ZKXRMIXYJLIJVIFTJ4BWS5R.json","graph_json":"https://pith.science/api/pith-number/K25ZKXRMIXYJLIJVIFTJ4BWS5R/graph.json","events_json":"https://pith.science/api/pith-number/K25ZKXRMIXYJLIJVIFTJ4BWS5R/events.json","paper":"https://pith.science/paper/K25ZKXRM"},"agent_actions":{"view_html":"https://pith.science/pith/K25ZKXRMIXYJLIJVIFTJ4BWS5R","download_json":"https://pith.science/pith/K25ZKXRMIXYJLIJVIFTJ4BWS5R.json","view_paper":"https://pith.science/paper/K25ZKXRM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.09337&json=true","fetch_graph":"https://pith.science/api/pith-number/K25ZKXRMIXYJLIJVIFTJ4BWS5R/graph.json","fetch_events":"https://pith.science/api/pith-number/K25ZKXRMIXYJLIJVIFTJ4BWS5R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K25ZKXRMIXYJLIJVIFTJ4BWS5R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K25ZKXRMIXYJLIJVIFTJ4BWS5R/action/storage_attestation","attest_author":"https://pith.science/pith/K25ZKXRMIXYJLIJVIFTJ4BWS5R/action/author_attestation","sign_citation":"https://pith.science/pith/K25ZKXRMIXYJLIJVIFTJ4BWS5R/action/citation_signature","submit_replication":"https://pith.science/pith/K25ZKXRMIXYJLIJVIFTJ4BWS5R/action/replication_record"}},"created_at":"2026-07-05T00:52:36.567881+00:00","updated_at":"2026-07-05T00:52:36.567881+00:00"}