{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:47DW4RQXDODIUM43XRJLDZISKD","short_pith_number":"pith:47DW4RQX","canonical_record":{"source":{"id":"2206.13108","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-06-27T08:25:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3060ab202e53f5f6e61d9616e315ff05ee8f2c690825fe412a75bf457adf3a6a","abstract_canon_sha256":"c7eec9f2a97e2a1af3c49330a10559c8323748b869fa49136a31783ecf388d87"},"schema_version":"1.0"},"canonical_sha256":"e7c76e46171b868a339bbc52b1e51250eba8e29c2a2375ee6160c8af3e06a519","source":{"kind":"arxiv","id":"2206.13108","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.13108","created_at":"2026-07-05T04:36:37Z"},{"alias_kind":"arxiv_version","alias_value":"2206.13108v2","created_at":"2026-07-05T04:36:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.13108","created_at":"2026-07-05T04:36:37Z"},{"alias_kind":"pith_short_12","alias_value":"47DW4RQXDODI","created_at":"2026-07-05T04:36:37Z"},{"alias_kind":"pith_short_16","alias_value":"47DW4RQXDODIUM43","created_at":"2026-07-05T04:36:37Z"},{"alias_kind":"pith_short_8","alias_value":"47DW4RQX","created_at":"2026-07-05T04:36:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:47DW4RQXDODIUM43XRJLDZISKD","target":"record","payload":{"canonical_record":{"source":{"id":"2206.13108","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-06-27T08:25:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3060ab202e53f5f6e61d9616e315ff05ee8f2c690825fe412a75bf457adf3a6a","abstract_canon_sha256":"c7eec9f2a97e2a1af3c49330a10559c8323748b869fa49136a31783ecf388d87"},"schema_version":"1.0"},"canonical_sha256":"e7c76e46171b868a339bbc52b1e51250eba8e29c2a2375ee6160c8af3e06a519","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:36:37.423218Z","signature_b64":"ab7xkagw2kBdhzH8ZLPrWOsMvKsOc8bu579yiCV0F5Xg4MpcEVuQZ10nDQCCtrNZR5aYhmP+t8DVucnV1lvbDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7c76e46171b868a339bbc52b1e51250eba8e29c2a2375ee6160c8af3e06a519","last_reissued_at":"2026-07-05T04:36:37.422742Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:36:37.422742Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2206.13108","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:36:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I4qzFSpQKcdBkSHo17zr2EUEF98jsGYcWAgovZNLtqr7o0bLjcDo7UUScraCWy5eTHHsUxopuwI1T3MaovDBBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-26T20:10:31.426856Z"},"content_sha256":"6723d00e0c8ecddc95cc09d8a7e31c2b8e63a8ce19daff41371630ad8c82c20f","schema_version":"1.0","event_id":"sha256:6723d00e0c8ecddc95cc09d8a7e31c2b8e63a8ce19daff41371630ad8c82c20f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:47DW4RQXDODIUM43XRJLDZISKD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Bo Zheng, Liang Wang, Penghui Wei, Shaoguo Liu, Xiaoyu Peng, Xuanhua Yang","submitted_at":"2022-06-27T08:25:06Z","abstract_excerpt":"Click-through rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have proved that learning a unified model to serve multiple domains is effective to improve the overall performance. However, it is still challenging to improve generalization across domains under limited training data, and hard to deploy current solutions due to their computational complexity. In this paper, we propose a simple yet effective framework AdaSparse for multi-domain CTR prediction, which learns adaptively sparse structure for each domain, achieving better genera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.13108","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/2206.13108/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:36:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rGKORHkHaFvlXOACp9ySLZ5UymbYD9qsdpOm4wzoLy8gaKJQbOjHjSg4OYiaT9xPflI9sxXIHY1ubQYMK6W6Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-26T20:10:31.427251Z"},"content_sha256":"f70645b1d41ea482edcdbb229f121f351d119ec2a43aa1aaa0192572e82b4605","schema_version":"1.0","event_id":"sha256:f70645b1d41ea482edcdbb229f121f351d119ec2a43aa1aaa0192572e82b4605"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/47DW4RQXDODIUM43XRJLDZISKD/bundle.json","state_url":"https://pith.science/pith/47DW4RQXDODIUM43XRJLDZISKD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/47DW4RQXDODIUM43XRJLDZISKD/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-26T20:10:31Z","links":{"resolver":"https://pith.science/pith/47DW4RQXDODIUM43XRJLDZISKD","bundle":"https://pith.science/pith/47DW4RQXDODIUM43XRJLDZISKD/bundle.json","state":"https://pith.science/pith/47DW4RQXDODIUM43XRJLDZISKD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/47DW4RQXDODIUM43XRJLDZISKD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:47DW4RQXDODIUM43XRJLDZISKD","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c7eec9f2a97e2a1af3c49330a10559c8323748b869fa49136a31783ecf388d87","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-06-27T08:25:06Z","title_canon_sha256":"3060ab202e53f5f6e61d9616e315ff05ee8f2c690825fe412a75bf457adf3a6a"},"schema_version":"1.0","source":{"id":"2206.13108","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.13108","created_at":"2026-07-05T04:36:37Z"},{"alias_kind":"arxiv_version","alias_value":"2206.13108v2","created_at":"2026-07-05T04:36:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.13108","created_at":"2026-07-05T04:36:37Z"},{"alias_kind":"pith_short_12","alias_value":"47DW4RQXDODI","created_at":"2026-07-05T04:36:37Z"},{"alias_kind":"pith_short_16","alias_value":"47DW4RQXDODIUM43","created_at":"2026-07-05T04:36:37Z"},{"alias_kind":"pith_short_8","alias_value":"47DW4RQX","created_at":"2026-07-05T04:36:37Z"}],"graph_snapshots":[{"event_id":"sha256:f70645b1d41ea482edcdbb229f121f351d119ec2a43aa1aaa0192572e82b4605","target":"graph","created_at":"2026-07-05T04:36:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2206.13108/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Click-through rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have proved that learning a unified model to serve multiple domains is effective to improve the overall performance. However, it is still challenging to improve generalization across domains under limited training data, and hard to deploy current solutions due to their computational complexity. In this paper, we propose a simple yet effective framework AdaSparse for multi-domain CTR prediction, which learns adaptively sparse structure for each domain, achieving better genera","authors_text":"Bo Zheng, Liang Wang, Penghui Wei, Shaoguo Liu, Xiaoyu Peng, Xuanhua Yang","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-06-27T08:25:06Z","title":"AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.13108","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6723d00e0c8ecddc95cc09d8a7e31c2b8e63a8ce19daff41371630ad8c82c20f","target":"record","created_at":"2026-07-05T04:36:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c7eec9f2a97e2a1af3c49330a10559c8323748b869fa49136a31783ecf388d87","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-06-27T08:25:06Z","title_canon_sha256":"3060ab202e53f5f6e61d9616e315ff05ee8f2c690825fe412a75bf457adf3a6a"},"schema_version":"1.0","source":{"id":"2206.13108","kind":"arxiv","version":2}},"canonical_sha256":"e7c76e46171b868a339bbc52b1e51250eba8e29c2a2375ee6160c8af3e06a519","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e7c76e46171b868a339bbc52b1e51250eba8e29c2a2375ee6160c8af3e06a519","first_computed_at":"2026-07-05T04:36:37.422742Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:36:37.422742Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ab7xkagw2kBdhzH8ZLPrWOsMvKsOc8bu579yiCV0F5Xg4MpcEVuQZ10nDQCCtrNZR5aYhmP+t8DVucnV1lvbDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:36:37.423218Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.13108","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6723d00e0c8ecddc95cc09d8a7e31c2b8e63a8ce19daff41371630ad8c82c20f","sha256:f70645b1d41ea482edcdbb229f121f351d119ec2a43aa1aaa0192572e82b4605"],"state_sha256":"63cea5c45dbabc3a9f12bafd77f40c88ae11e6b3de1ae3db9ebf6e50868828a1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zEbeJcdqNBP8wi7QN6GDf5GhzLAO2fqZOAsD3SBiC5fC2DJIPS3xNhPTv4ux3reAsAhs5oWfRMLaSQyNcn2bDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-26T20:10:31.430021Z","bundle_sha256":"2c0df80fff3135f1332de5dd4e760011b0164eef1c194ed7ae6b169688009980"}}