{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IHC6KCLYL5KDB4ERL7L3TIISTG","short_pith_number":"pith:IHC6KCLY","schema_version":"1.0","canonical_sha256":"41c5e509785f5430f0915fd7b9a11299b28f92c86fae8c592b3d3d91057fd06e","source":{"kind":"arxiv","id":"2408.11372","version":1},"attestation_state":"computed","paper":{"title":"Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Defu Lian, Enhong Chen, Hao Wang, Kai Cheng, Kefan Wang, Wei Guo, Yong Liu, Yongqiang Han, Zhen Wang","submitted_at":"2024-08-21T06:48:38Z","abstract_excerpt":"In the realm of recommendation systems, users exhibit a diverse array of behaviors when interacting with items. This phenomenon has spurred research into learning the implicit semantic relationships between these behaviors to enhance recommendation performance. However, these methods often entail high computational complexity. To address concerns regarding efficiency, pre-training presents a viable solution. Its objective is to extract knowledge from extensive pre-training data and fine-tune the model for downstream tasks. Nevertheless, previous pre-training methods have primarily focused on s"},"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":"2408.11372","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-08-21T06:48:38Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6b09179958d641031cd1a862345267974fdaefac4a9612fe5135055eb5cda905","abstract_canon_sha256":"d1452facaed797c3ac9bc0f6e7b136444e298b5901bd21f471fa37008ad9c10d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:57:42.837999Z","signature_b64":"gLkokK24tmcTu0qjO0ylvVGOopJwEzbt4aUzaKrW4EIbkYq6Wr1Pese45HIzMaRbMwEVPUJs0QpsMzzuilOWBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41c5e509785f5430f0915fd7b9a11299b28f92c86fae8c592b3d3d91057fd06e","last_reissued_at":"2026-07-05T08:57:42.837398Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:57:42.837398Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Defu Lian, Enhong Chen, Hao Wang, Kai Cheng, Kefan Wang, Wei Guo, Yong Liu, Yongqiang Han, Zhen Wang","submitted_at":"2024-08-21T06:48:38Z","abstract_excerpt":"In the realm of recommendation systems, users exhibit a diverse array of behaviors when interacting with items. This phenomenon has spurred research into learning the implicit semantic relationships between these behaviors to enhance recommendation performance. However, these methods often entail high computational complexity. To address concerns regarding efficiency, pre-training presents a viable solution. Its objective is to extract knowledge from extensive pre-training data and fine-tune the model for downstream tasks. Nevertheless, previous pre-training methods have primarily focused on s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.11372","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/2408.11372/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":"2408.11372","created_at":"2026-07-05T08:57:42.837525+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.11372v1","created_at":"2026-07-05T08:57:42.837525+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.11372","created_at":"2026-07-05T08:57:42.837525+00:00"},{"alias_kind":"pith_short_12","alias_value":"IHC6KCLYL5KD","created_at":"2026-07-05T08:57:42.837525+00:00"},{"alias_kind":"pith_short_16","alias_value":"IHC6KCLYL5KDB4ER","created_at":"2026-07-05T08:57:42.837525+00:00"},{"alias_kind":"pith_short_8","alias_value":"IHC6KCLY","created_at":"2026-07-05T08:57:42.837525+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/IHC6KCLYL5KDB4ERL7L3TIISTG","json":"https://pith.science/pith/IHC6KCLYL5KDB4ERL7L3TIISTG.json","graph_json":"https://pith.science/api/pith-number/IHC6KCLYL5KDB4ERL7L3TIISTG/graph.json","events_json":"https://pith.science/api/pith-number/IHC6KCLYL5KDB4ERL7L3TIISTG/events.json","paper":"https://pith.science/paper/IHC6KCLY"},"agent_actions":{"view_html":"https://pith.science/pith/IHC6KCLYL5KDB4ERL7L3TIISTG","download_json":"https://pith.science/pith/IHC6KCLYL5KDB4ERL7L3TIISTG.json","view_paper":"https://pith.science/paper/IHC6KCLY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.11372&json=true","fetch_graph":"https://pith.science/api/pith-number/IHC6KCLYL5KDB4ERL7L3TIISTG/graph.json","fetch_events":"https://pith.science/api/pith-number/IHC6KCLYL5KDB4ERL7L3TIISTG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IHC6KCLYL5KDB4ERL7L3TIISTG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IHC6KCLYL5KDB4ERL7L3TIISTG/action/storage_attestation","attest_author":"https://pith.science/pith/IHC6KCLYL5KDB4ERL7L3TIISTG/action/author_attestation","sign_citation":"https://pith.science/pith/IHC6KCLYL5KDB4ERL7L3TIISTG/action/citation_signature","submit_replication":"https://pith.science/pith/IHC6KCLYL5KDB4ERL7L3TIISTG/action/replication_record"}},"created_at":"2026-07-05T08:57:42.837525+00:00","updated_at":"2026-07-05T08:57:42.837525+00:00"}