{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XDLPO6UJWOPBSBG6VKCYQ4K5GP","short_pith_number":"pith:XDLPO6UJ","schema_version":"1.0","canonical_sha256":"b8d6f77a89b39e1904deaa8588715d33d313423f3d5459813a2345cd097f4f42","source":{"kind":"arxiv","id":"2408.02691","version":1},"attestation_state":"computed","paper":{"title":"Symmetric Graph Contrastive Learning against Noisy Views for Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR"],"primary_cat":"cs.LG","authors_text":"Chu Zhao, Enneng Yang, Guibing Guo, Jianzhe Zhao, Xingwei Wang, Yuliang Liang","submitted_at":"2024-08-03T06:58:07Z","abstract_excerpt":"Graph Contrastive Learning (GCL) leverages data augmentation techniques to produce contrasting views, enhancing the accuracy of recommendation systems through learning the consistency between contrastive views. However, existing augmentation methods, such as directly perturbing interaction graph (e.g., node/edge dropout), may interfere with the original connections and generate poor contrasting views, resulting in sub-optimal performance. In this paper, we define the views that share only a small amount of information with the original graph due to poor data augmentation as noisy views (i.e., "},"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":"2408.02691","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-08-03T06:58:07Z","cross_cats_sorted":["cs.AI","cs.IR"],"title_canon_sha256":"e6a90488821504cd6cbcca51c9f35d26a998f8de94ffc787aafce15896e38fac","abstract_canon_sha256":"2dc83364bd2490952de1feb8850a41d53b35d106efb0be9bad7ea2b00d27af46"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:52:38.656543Z","signature_b64":"JRJoaPZFXrGpEA/mjy0jt3J2aDdytcFKi7fb8/Q4HJ2PQzyvNOgIrzT11wVUv9+EIvRqPVosON7Mzxyut/sfCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b8d6f77a89b39e1904deaa8588715d33d313423f3d5459813a2345cd097f4f42","last_reissued_at":"2026-07-05T08:52:38.656104Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:52:38.656104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Symmetric Graph Contrastive Learning against Noisy Views for Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR"],"primary_cat":"cs.LG","authors_text":"Chu Zhao, Enneng Yang, Guibing Guo, Jianzhe Zhao, Xingwei Wang, Yuliang Liang","submitted_at":"2024-08-03T06:58:07Z","abstract_excerpt":"Graph Contrastive Learning (GCL) leverages data augmentation techniques to produce contrasting views, enhancing the accuracy of recommendation systems through learning the consistency between contrastive views. However, existing augmentation methods, such as directly perturbing interaction graph (e.g., node/edge dropout), may interfere with the original connections and generate poor contrasting views, resulting in sub-optimal performance. In this paper, we define the views that share only a small amount of information with the original graph due to poor data augmentation as noisy views (i.e., "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.02691","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/2408.02691/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":"2408.02691","created_at":"2026-07-05T08:52:38.656164+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.02691v1","created_at":"2026-07-05T08:52:38.656164+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.02691","created_at":"2026-07-05T08:52:38.656164+00:00"},{"alias_kind":"pith_short_12","alias_value":"XDLPO6UJWOPB","created_at":"2026-07-05T08:52:38.656164+00:00"},{"alias_kind":"pith_short_16","alias_value":"XDLPO6UJWOPBSBG6","created_at":"2026-07-05T08:52:38.656164+00:00"},{"alias_kind":"pith_short_8","alias_value":"XDLPO6UJ","created_at":"2026-07-05T08:52:38.656164+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04061","citing_title":"Intra-Modal Neighbors Never Lie: Rectifying Inter-Modal Noisy Correspondence via Graph-Based Intra-Modal Reasoning","ref_index":162,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XDLPO6UJWOPBSBG6VKCYQ4K5GP","json":"https://pith.science/pith/XDLPO6UJWOPBSBG6VKCYQ4K5GP.json","graph_json":"https://pith.science/api/pith-number/XDLPO6UJWOPBSBG6VKCYQ4K5GP/graph.json","events_json":"https://pith.science/api/pith-number/XDLPO6UJWOPBSBG6VKCYQ4K5GP/events.json","paper":"https://pith.science/paper/XDLPO6UJ"},"agent_actions":{"view_html":"https://pith.science/pith/XDLPO6UJWOPBSBG6VKCYQ4K5GP","download_json":"https://pith.science/pith/XDLPO6UJWOPBSBG6VKCYQ4K5GP.json","view_paper":"https://pith.science/paper/XDLPO6UJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.02691&json=true","fetch_graph":"https://pith.science/api/pith-number/XDLPO6UJWOPBSBG6VKCYQ4K5GP/graph.json","fetch_events":"https://pith.science/api/pith-number/XDLPO6UJWOPBSBG6VKCYQ4K5GP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XDLPO6UJWOPBSBG6VKCYQ4K5GP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XDLPO6UJWOPBSBG6VKCYQ4K5GP/action/storage_attestation","attest_author":"https://pith.science/pith/XDLPO6UJWOPBSBG6VKCYQ4K5GP/action/author_attestation","sign_citation":"https://pith.science/pith/XDLPO6UJWOPBSBG6VKCYQ4K5GP/action/citation_signature","submit_replication":"https://pith.science/pith/XDLPO6UJWOPBSBG6VKCYQ4K5GP/action/replication_record"}},"created_at":"2026-07-05T08:52:38.656164+00:00","updated_at":"2026-07-05T08:52:38.656164+00:00"}