{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CRDHBONLIRZEM7ZPUK3HSNWDXN","short_pith_number":"pith:CRDHBONL","schema_version":"1.0","canonical_sha256":"144670b9ab4472467f2fa2b67936c3bb77dc9cde019c675c142ad4acd1bb8b2c","source":{"kind":"arxiv","id":"2411.12989","version":2},"attestation_state":"computed","paper":{"title":"Data Watermarking for Sequential Recommender Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Cheng Long, Hongxu Chen, Hongzhi Yin, Sixiao Zhang, Wei Yuan","submitted_at":"2024-11-20T02:34:21Z","abstract_excerpt":"In the era of large foundation models, data has become a crucial component in building high-performance AI systems. As the demand for high-quality and large-scale data continues to rise, data copyright protection is attracting increasing attention. In this work, we explore the problem of data watermarking for sequential recommender systems, where a watermark is embedded into the target dataset and can be detected in models trained on that dataset. We focus on two settings: dataset watermarking, which protects the ownership of the entire dataset, and user watermarking, which safeguards the data"},"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":"2411.12989","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-11-20T02:34:21Z","cross_cats_sorted":[],"title_canon_sha256":"e5ad3924e4db7f4f6a714908ede54d74b3f2893adf74ba0cd26ca1361ef65f4a","abstract_canon_sha256":"5f0c00696544c21b859e562a7fd76f11138f4301948e736aa96866c0921b3dee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:04:41.886697Z","signature_b64":"jLoecDeYWcSB0d+usCo4WCgh4dVtM/BrhH/NxI/FgLm0nVz2p+mLauspgxoafH0VG2B+efwc5E4fXqClquaLAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"144670b9ab4472467f2fa2b67936c3bb77dc9cde019c675c142ad4acd1bb8b2c","last_reissued_at":"2026-07-05T11:04:41.886216Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:04:41.886216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data Watermarking for Sequential Recommender Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Cheng Long, Hongxu Chen, Hongzhi Yin, Sixiao Zhang, Wei Yuan","submitted_at":"2024-11-20T02:34:21Z","abstract_excerpt":"In the era of large foundation models, data has become a crucial component in building high-performance AI systems. As the demand for high-quality and large-scale data continues to rise, data copyright protection is attracting increasing attention. In this work, we explore the problem of data watermarking for sequential recommender systems, where a watermark is embedded into the target dataset and can be detected in models trained on that dataset. We focus on two settings: dataset watermarking, which protects the ownership of the entire dataset, and user watermarking, which safeguards the data"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.12989","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/2411.12989/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":"2411.12989","created_at":"2026-07-05T11:04:41.886277+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.12989v2","created_at":"2026-07-05T11:04:41.886277+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.12989","created_at":"2026-07-05T11:04:41.886277+00:00"},{"alias_kind":"pith_short_12","alias_value":"CRDHBONLIRZE","created_at":"2026-07-05T11:04:41.886277+00:00"},{"alias_kind":"pith_short_16","alias_value":"CRDHBONLIRZEM7ZP","created_at":"2026-07-05T11:04:41.886277+00:00"},{"alias_kind":"pith_short_8","alias_value":"CRDHBONL","created_at":"2026-07-05T11:04:41.886277+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/CRDHBONLIRZEM7ZPUK3HSNWDXN","json":"https://pith.science/pith/CRDHBONLIRZEM7ZPUK3HSNWDXN.json","graph_json":"https://pith.science/api/pith-number/CRDHBONLIRZEM7ZPUK3HSNWDXN/graph.json","events_json":"https://pith.science/api/pith-number/CRDHBONLIRZEM7ZPUK3HSNWDXN/events.json","paper":"https://pith.science/paper/CRDHBONL"},"agent_actions":{"view_html":"https://pith.science/pith/CRDHBONLIRZEM7ZPUK3HSNWDXN","download_json":"https://pith.science/pith/CRDHBONLIRZEM7ZPUK3HSNWDXN.json","view_paper":"https://pith.science/paper/CRDHBONL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.12989&json=true","fetch_graph":"https://pith.science/api/pith-number/CRDHBONLIRZEM7ZPUK3HSNWDXN/graph.json","fetch_events":"https://pith.science/api/pith-number/CRDHBONLIRZEM7ZPUK3HSNWDXN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CRDHBONLIRZEM7ZPUK3HSNWDXN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CRDHBONLIRZEM7ZPUK3HSNWDXN/action/storage_attestation","attest_author":"https://pith.science/pith/CRDHBONLIRZEM7ZPUK3HSNWDXN/action/author_attestation","sign_citation":"https://pith.science/pith/CRDHBONLIRZEM7ZPUK3HSNWDXN/action/citation_signature","submit_replication":"https://pith.science/pith/CRDHBONLIRZEM7ZPUK3HSNWDXN/action/replication_record"}},"created_at":"2026-07-05T11:04:41.886277+00:00","updated_at":"2026-07-05T11:04:41.886277+00:00"}