{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VOLMQRSSJZKVCSKZWORG2SSMSK","short_pith_number":"pith:VOLMQRSS","schema_version":"1.0","canonical_sha256":"ab96c846524e55514959b3a26d4a4c92bf97c8b8bbf6a8042d580220e1766228","source":{"kind":"arxiv","id":"2412.15579","version":2},"attestation_state":"computed","paper":{"title":"Score-based Generative Diffusion Models for Social Recommendations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.SI","authors_text":"Chengyi Liu, Jiahao Zhang, Qing Li, Shijie Wang, Wenqi Fan","submitted_at":"2024-12-20T05:23:45Z","abstract_excerpt":"With the prevalence of social networks on online platforms, social recommendation has become a vital technique for enhancing personalized recommendations. The effectiveness of social recommendations largely relies on the social homophily assumption, which presumes that individuals with social connections often share similar preferences. However, this foundational premise has been recently challenged due to the inherent complexity and noise present in real-world social networks. In this paper, we tackle the low social homophily challenge from an innovative generative perspective, directly gener"},"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":"2412.15579","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2024-12-20T05:23:45Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"771b6fdabba88a16dfc0f3bff75da7b30a2e42d5326fcfe8d2fb0cc6759ee9a8","abstract_canon_sha256":"7ae649cab4ef2965e51cbbbc24a2080b2ccfc1a43bd257aefb43606794d7d726"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:59:56.329482Z","signature_b64":"lE4gDuNatGP9q5xQ2dtho6hchiZvjoS37vhEwuDaaGBn5PNKENlsebduSZqc4BSXPyq5rkh0+ZOX3Oenezt/Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab96c846524e55514959b3a26d4a4c92bf97c8b8bbf6a8042d580220e1766228","last_reissued_at":"2026-07-05T11:59:56.328991Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:59:56.328991Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Score-based Generative Diffusion Models for Social Recommendations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.SI","authors_text":"Chengyi Liu, Jiahao Zhang, Qing Li, Shijie Wang, Wenqi Fan","submitted_at":"2024-12-20T05:23:45Z","abstract_excerpt":"With the prevalence of social networks on online platforms, social recommendation has become a vital technique for enhancing personalized recommendations. The effectiveness of social recommendations largely relies on the social homophily assumption, which presumes that individuals with social connections often share similar preferences. However, this foundational premise has been recently challenged due to the inherent complexity and noise present in real-world social networks. In this paper, we tackle the low social homophily challenge from an innovative generative perspective, directly gener"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.15579","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/2412.15579/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":"2412.15579","created_at":"2026-07-05T11:59:56.329049+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.15579v2","created_at":"2026-07-05T11:59:56.329049+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.15579","created_at":"2026-07-05T11:59:56.329049+00:00"},{"alias_kind":"pith_short_12","alias_value":"VOLMQRSSJZKV","created_at":"2026-07-05T11:59:56.329049+00:00"},{"alias_kind":"pith_short_16","alias_value":"VOLMQRSSJZKVCSKZ","created_at":"2026-07-05T11:59:56.329049+00:00"},{"alias_kind":"pith_short_8","alias_value":"VOLMQRSS","created_at":"2026-07-05T11:59:56.329049+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.16284","citing_title":"Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse","ref_index":49,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VOLMQRSSJZKVCSKZWORG2SSMSK","json":"https://pith.science/pith/VOLMQRSSJZKVCSKZWORG2SSMSK.json","graph_json":"https://pith.science/api/pith-number/VOLMQRSSJZKVCSKZWORG2SSMSK/graph.json","events_json":"https://pith.science/api/pith-number/VOLMQRSSJZKVCSKZWORG2SSMSK/events.json","paper":"https://pith.science/paper/VOLMQRSS"},"agent_actions":{"view_html":"https://pith.science/pith/VOLMQRSSJZKVCSKZWORG2SSMSK","download_json":"https://pith.science/pith/VOLMQRSSJZKVCSKZWORG2SSMSK.json","view_paper":"https://pith.science/paper/VOLMQRSS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.15579&json=true","fetch_graph":"https://pith.science/api/pith-number/VOLMQRSSJZKVCSKZWORG2SSMSK/graph.json","fetch_events":"https://pith.science/api/pith-number/VOLMQRSSJZKVCSKZWORG2SSMSK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VOLMQRSSJZKVCSKZWORG2SSMSK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VOLMQRSSJZKVCSKZWORG2SSMSK/action/storage_attestation","attest_author":"https://pith.science/pith/VOLMQRSSJZKVCSKZWORG2SSMSK/action/author_attestation","sign_citation":"https://pith.science/pith/VOLMQRSSJZKVCSKZWORG2SSMSK/action/citation_signature","submit_replication":"https://pith.science/pith/VOLMQRSSJZKVCSKZWORG2SSMSK/action/replication_record"}},"created_at":"2026-07-05T11:59:56.329049+00:00","updated_at":"2026-07-05T11:59:56.329049+00:00"}