{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:P5CO2QA5LV5VQZOLDAKVMEPWE7","short_pith_number":"pith:P5CO2QA5","schema_version":"1.0","canonical_sha256":"7f44ed401d5d7b5865cb18155611f627cc9e28a5c612c681bcef113cfb155f86","source":{"kind":"arxiv","id":"2309.02061","version":1},"attestation_state":"computed","paper":{"title":"Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Bo Chen, Huifeng Guo, Jingtong Gao, Menghui Zhu, Ruiming Tang, Xiangyu Zhao, Xiaopeng Li, Yichao Wang, Yuhao Wang","submitted_at":"2023-09-05T09:01:47Z","abstract_excerpt":"Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations contributes to strengthening information sharing and improving overall performance. However, existing multi-scenario models only consider coarse-grained explicit scenario modeling that depends on pre-defined scenario identification from manual prior rules, which is biased and sub-optimal. To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRe"},"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":"2309.02061","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-09-05T09:01:47Z","cross_cats_sorted":[],"title_canon_sha256":"b471ac6281ddfbfd5b6b6208e0510924b48e30de689e524e79c4dd6ed42ccddf","abstract_canon_sha256":"627ca36afaf6fe57eb89b50104513e55ab067b10e84ae68ae3c57221504638c2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:47:47.342131Z","signature_b64":"Z1Z8M75yt3QHjWtqE3+zcc931jBdNJO5nn1Dc+LGnN95WUhPojhbUeib9akdjVtZ60van5/aUCFmR4QKFUWhDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f44ed401d5d7b5865cb18155611f627cc9e28a5c612c681bcef113cfb155f86","last_reissued_at":"2026-07-05T06:47:47.341529Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:47:47.341529Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Bo Chen, Huifeng Guo, Jingtong Gao, Menghui Zhu, Ruiming Tang, Xiangyu Zhao, Xiaopeng Li, Yichao Wang, Yuhao Wang","submitted_at":"2023-09-05T09:01:47Z","abstract_excerpt":"Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations contributes to strengthening information sharing and improving overall performance. However, existing multi-scenario models only consider coarse-grained explicit scenario modeling that depends on pre-defined scenario identification from manual prior rules, which is biased and sub-optimal. To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.02061","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/2309.02061/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":"2309.02061","created_at":"2026-07-05T06:47:47.341586+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.02061v1","created_at":"2026-07-05T06:47:47.341586+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.02061","created_at":"2026-07-05T06:47:47.341586+00:00"},{"alias_kind":"pith_short_12","alias_value":"P5CO2QA5LV5V","created_at":"2026-07-05T06:47:47.341586+00:00"},{"alias_kind":"pith_short_16","alias_value":"P5CO2QA5LV5VQZOL","created_at":"2026-07-05T06:47:47.341586+00:00"},{"alias_kind":"pith_short_8","alias_value":"P5CO2QA5","created_at":"2026-07-05T06:47:47.341586+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2508.06550","citing_title":"Generative Bid Shading in Real-Time Bidding Advertising","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P5CO2QA5LV5VQZOLDAKVMEPWE7","json":"https://pith.science/pith/P5CO2QA5LV5VQZOLDAKVMEPWE7.json","graph_json":"https://pith.science/api/pith-number/P5CO2QA5LV5VQZOLDAKVMEPWE7/graph.json","events_json":"https://pith.science/api/pith-number/P5CO2QA5LV5VQZOLDAKVMEPWE7/events.json","paper":"https://pith.science/paper/P5CO2QA5"},"agent_actions":{"view_html":"https://pith.science/pith/P5CO2QA5LV5VQZOLDAKVMEPWE7","download_json":"https://pith.science/pith/P5CO2QA5LV5VQZOLDAKVMEPWE7.json","view_paper":"https://pith.science/paper/P5CO2QA5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.02061&json=true","fetch_graph":"https://pith.science/api/pith-number/P5CO2QA5LV5VQZOLDAKVMEPWE7/graph.json","fetch_events":"https://pith.science/api/pith-number/P5CO2QA5LV5VQZOLDAKVMEPWE7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P5CO2QA5LV5VQZOLDAKVMEPWE7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P5CO2QA5LV5VQZOLDAKVMEPWE7/action/storage_attestation","attest_author":"https://pith.science/pith/P5CO2QA5LV5VQZOLDAKVMEPWE7/action/author_attestation","sign_citation":"https://pith.science/pith/P5CO2QA5LV5VQZOLDAKVMEPWE7/action/citation_signature","submit_replication":"https://pith.science/pith/P5CO2QA5LV5VQZOLDAKVMEPWE7/action/replication_record"}},"created_at":"2026-07-05T06:47:47.341586+00:00","updated_at":"2026-07-05T06:47:47.341586+00:00"}