{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5TQHOX2TE2WAWXS2R4A2SO4NVH","short_pith_number":"pith:5TQHOX2T","schema_version":"1.0","canonical_sha256":"ece0775f5326ac0b5e5a8f01a93b8da9c13bb06addb2729f5e211d63d349ebf2","source":{"kind":"arxiv","id":"2406.13235","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Hongke Zhao, Jianpin Fan, Likang Wu, Ming He, Zhong Guan","submitted_at":"2024-06-19T05:50:15Z","abstract_excerpt":"Large Language Models (LLMs) are increasingly prominent in the recommendation systems domain. Existing studies usually utilize in-context learning or supervised fine-tuning on task-specific data to align LLMs into recommendations. However, the substantial bias in semantic spaces between language processing tasks and recommendation tasks poses a nonnegligible challenge. Specifically, without the adequate capturing ability of collaborative information, existing modeling paradigms struggle to capture behavior patterns within community groups, leading to LLMs' ineffectiveness in discerning implici"},"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":"2406.13235","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-06-19T05:50:15Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0583beba61ee605490707e107d97b8a718032a50131c72ea493c64bb69f11fc4","abstract_canon_sha256":"897cfc2f14047d54256f403afc1a985f0756fe3a2bb240e9ffe2eed571203c2f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:34:27.235572Z","signature_b64":"5qoV1YYZtH6lNRaLWKZkkpCS1tcPwOSar02MC22TQKfx/Nie2g7QruO4+kXJeQHVeO/8iz2x5k4mia0TZZj5CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ece0775f5326ac0b5e5a8f01a93b8da9c13bb06addb2729f5e211d63d349ebf2","last_reissued_at":"2026-07-05T08:34:27.235094Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:34:27.235094Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Hongke Zhao, Jianpin Fan, Likang Wu, Ming He, Zhong Guan","submitted_at":"2024-06-19T05:50:15Z","abstract_excerpt":"Large Language Models (LLMs) are increasingly prominent in the recommendation systems domain. Existing studies usually utilize in-context learning or supervised fine-tuning on task-specific data to align LLMs into recommendations. However, the substantial bias in semantic spaces between language processing tasks and recommendation tasks poses a nonnegligible challenge. Specifically, without the adequate capturing ability of collaborative information, existing modeling paradigms struggle to capture behavior patterns within community groups, leading to LLMs' ineffectiveness in discerning implici"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.13235","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/2406.13235/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":"2406.13235","created_at":"2026-07-05T08:34:27.235152+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.13235v1","created_at":"2026-07-05T08:34:27.235152+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.13235","created_at":"2026-07-05T08:34:27.235152+00:00"},{"alias_kind":"pith_short_12","alias_value":"5TQHOX2TE2WA","created_at":"2026-07-05T08:34:27.235152+00:00"},{"alias_kind":"pith_short_16","alias_value":"5TQHOX2TE2WAWXS2","created_at":"2026-07-05T08:34:27.235152+00:00"},{"alias_kind":"pith_short_8","alias_value":"5TQHOX2T","created_at":"2026-07-05T08:34:27.235152+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.08346","citing_title":"Graph Foundation Models for Recommendation: A Comprehensive Survey","ref_index":2023,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5TQHOX2TE2WAWXS2R4A2SO4NVH","json":"https://pith.science/pith/5TQHOX2TE2WAWXS2R4A2SO4NVH.json","graph_json":"https://pith.science/api/pith-number/5TQHOX2TE2WAWXS2R4A2SO4NVH/graph.json","events_json":"https://pith.science/api/pith-number/5TQHOX2TE2WAWXS2R4A2SO4NVH/events.json","paper":"https://pith.science/paper/5TQHOX2T"},"agent_actions":{"view_html":"https://pith.science/pith/5TQHOX2TE2WAWXS2R4A2SO4NVH","download_json":"https://pith.science/pith/5TQHOX2TE2WAWXS2R4A2SO4NVH.json","view_paper":"https://pith.science/paper/5TQHOX2T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.13235&json=true","fetch_graph":"https://pith.science/api/pith-number/5TQHOX2TE2WAWXS2R4A2SO4NVH/graph.json","fetch_events":"https://pith.science/api/pith-number/5TQHOX2TE2WAWXS2R4A2SO4NVH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5TQHOX2TE2WAWXS2R4A2SO4NVH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5TQHOX2TE2WAWXS2R4A2SO4NVH/action/storage_attestation","attest_author":"https://pith.science/pith/5TQHOX2TE2WAWXS2R4A2SO4NVH/action/author_attestation","sign_citation":"https://pith.science/pith/5TQHOX2TE2WAWXS2R4A2SO4NVH/action/citation_signature","submit_replication":"https://pith.science/pith/5TQHOX2TE2WAWXS2R4A2SO4NVH/action/replication_record"}},"created_at":"2026-07-05T08:34:27.235152+00:00","updated_at":"2026-07-05T08:34:27.235152+00:00"}