{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DFQCHM23JRGUWV4KHIQYVQASP4","short_pith_number":"pith:DFQCHM23","schema_version":"1.0","canonical_sha256":"196023b35b4c4d4b578a3a218ac0127f31c68deda5df76d456e6a3bf288485cf","source":{"kind":"arxiv","id":"2504.11889","version":2},"attestation_state":"computed","paper":{"title":"Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Donghee Han, Hwanjun Song, Mun Yong Yi","submitted_at":"2025-04-16T09:17:45Z","abstract_excerpt":"Recent studies have explored integrating large language models (LLMs) into recommendation systems but face several challenges, including training-induced bias and bottlenecks from serialized architecture. To effectively address these issues, we propose a Query-toRecommendation, a parallel recommendation framework that decouples LLMs from candidate pre-selection and instead enables direct retrieval over the entire item pool. Our framework connects LLMs and recommendation models in a parallel manner, allowing each component to independently utilize its strengths without interfering with the othe"},"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":"2504.11889","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2025-04-16T09:17:45Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"c4441da235d15f1b4f9d9622ad7618ce2f49941d675d0353def0f1c3ac0cb375","abstract_canon_sha256":"ee60d70ff1c9a1bdd3e08dc02500591793fd669cba978718bcd0e5a355961029"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:11:17.032627Z","signature_b64":"wYuqZNom1eskD1QGZ6jcuSejabRsWEo7mRTPaIcyy9Upts4HdUgyvR2LLu/oPzSvOCtZTtDpa+NfpH1m3x75Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"196023b35b4c4d4b578a3a218ac0127f31c68deda5df76d456e6a3bf288485cf","last_reissued_at":"2026-07-05T12:11:17.031824Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:11:17.031824Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Donghee Han, Hwanjun Song, Mun Yong Yi","submitted_at":"2025-04-16T09:17:45Z","abstract_excerpt":"Recent studies have explored integrating large language models (LLMs) into recommendation systems but face several challenges, including training-induced bias and bottlenecks from serialized architecture. To effectively address these issues, we propose a Query-toRecommendation, a parallel recommendation framework that decouples LLMs from candidate pre-selection and instead enables direct retrieval over the entire item pool. Our framework connects LLMs and recommendation models in a parallel manner, allowing each component to independently utilize its strengths without interfering with the othe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.11889","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/2504.11889/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":"2504.11889","created_at":"2026-07-05T12:11:17.031932+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.11889v2","created_at":"2026-07-05T12:11:17.031932+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.11889","created_at":"2026-07-05T12:11:17.031932+00:00"},{"alias_kind":"pith_short_12","alias_value":"DFQCHM23JRGU","created_at":"2026-07-05T12:11:17.031932+00:00"},{"alias_kind":"pith_short_16","alias_value":"DFQCHM23JRGUWV4K","created_at":"2026-07-05T12:11:17.031932+00:00"},{"alias_kind":"pith_short_8","alias_value":"DFQCHM23","created_at":"2026-07-05T12:11:17.031932+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/DFQCHM23JRGUWV4KHIQYVQASP4","json":"https://pith.science/pith/DFQCHM23JRGUWV4KHIQYVQASP4.json","graph_json":"https://pith.science/api/pith-number/DFQCHM23JRGUWV4KHIQYVQASP4/graph.json","events_json":"https://pith.science/api/pith-number/DFQCHM23JRGUWV4KHIQYVQASP4/events.json","paper":"https://pith.science/paper/DFQCHM23"},"agent_actions":{"view_html":"https://pith.science/pith/DFQCHM23JRGUWV4KHIQYVQASP4","download_json":"https://pith.science/pith/DFQCHM23JRGUWV4KHIQYVQASP4.json","view_paper":"https://pith.science/paper/DFQCHM23","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.11889&json=true","fetch_graph":"https://pith.science/api/pith-number/DFQCHM23JRGUWV4KHIQYVQASP4/graph.json","fetch_events":"https://pith.science/api/pith-number/DFQCHM23JRGUWV4KHIQYVQASP4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DFQCHM23JRGUWV4KHIQYVQASP4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DFQCHM23JRGUWV4KHIQYVQASP4/action/storage_attestation","attest_author":"https://pith.science/pith/DFQCHM23JRGUWV4KHIQYVQASP4/action/author_attestation","sign_citation":"https://pith.science/pith/DFQCHM23JRGUWV4KHIQYVQASP4/action/citation_signature","submit_replication":"https://pith.science/pith/DFQCHM23JRGUWV4KHIQYVQASP4/action/replication_record"}},"created_at":"2026-07-05T12:11:17.031932+00:00","updated_at":"2026-07-05T12:11:17.031932+00:00"}