{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:32UUEQARLAA3EG4KOUYACN7XOF","short_pith_number":"pith:32UUEQAR","schema_version":"1.0","canonical_sha256":"dea94240115801b21b8a75300137f7716ee168f63394f0100e055c1271a8f91c","source":{"kind":"arxiv","id":"2507.07251","version":1},"attestation_state":"computed","paper":{"title":"A Language-Driven Framework for Improving Personalized Recommendations: Merging LLMs with Traditional Algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.IR","authors_text":"Aaron Goldstein, Ayan Dutta","submitted_at":"2025-07-09T19:48:33Z","abstract_excerpt":"Traditional recommendation algorithms are not designed to provide personalized recommendations based on user preferences provided through text, e.g., \"I enjoy light-hearted comedies with a lot of humor\". Large Language Models (LLMs) have emerged as one of the most promising tools for natural language processing in recent years. This research proposes a novel framework that mimics how a close friend would recommend items based on their knowledge of an individual's tastes. We leverage LLMs to enhance movie recommendation systems by refining traditional algorithm outputs and integrating them with"},"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":"2507.07251","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-07-09T19:48:33Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"9a8cfacbb7ba6ec1d449ac0c1456e51103c157f0f7323c8922c01ade03efc51c","abstract_canon_sha256":"988508d76c75e4ad4d6612318c1902aa90af932d0fb97aa472bb0a7476221c70"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:34:34.213335Z","signature_b64":"/7w3o+W0s5bHaCkkv2URSPWR23dij2DVrrIFq1lq/bu04sgsGd/HiB7wJ4fPT6uVQ7+1yXuwIwimHcJU8HtsAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dea94240115801b21b8a75300137f7716ee168f63394f0100e055c1271a8f91c","last_reissued_at":"2026-07-05T11:34:34.212945Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:34:34.212945Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Language-Driven Framework for Improving Personalized Recommendations: Merging LLMs with Traditional Algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.IR","authors_text":"Aaron Goldstein, Ayan Dutta","submitted_at":"2025-07-09T19:48:33Z","abstract_excerpt":"Traditional recommendation algorithms are not designed to provide personalized recommendations based on user preferences provided through text, e.g., \"I enjoy light-hearted comedies with a lot of humor\". Large Language Models (LLMs) have emerged as one of the most promising tools for natural language processing in recent years. This research proposes a novel framework that mimics how a close friend would recommend items based on their knowledge of an individual's tastes. We leverage LLMs to enhance movie recommendation systems by refining traditional algorithm outputs and integrating them with"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.07251","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/2507.07251/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":"2507.07251","created_at":"2026-07-05T11:34:34.213000+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.07251v1","created_at":"2026-07-05T11:34:34.213000+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.07251","created_at":"2026-07-05T11:34:34.213000+00:00"},{"alias_kind":"pith_short_12","alias_value":"32UUEQARLAA3","created_at":"2026-07-05T11:34:34.213000+00:00"},{"alias_kind":"pith_short_16","alias_value":"32UUEQARLAA3EG4K","created_at":"2026-07-05T11:34:34.213000+00:00"},{"alias_kind":"pith_short_8","alias_value":"32UUEQAR","created_at":"2026-07-05T11:34:34.213000+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/32UUEQARLAA3EG4KOUYACN7XOF","json":"https://pith.science/pith/32UUEQARLAA3EG4KOUYACN7XOF.json","graph_json":"https://pith.science/api/pith-number/32UUEQARLAA3EG4KOUYACN7XOF/graph.json","events_json":"https://pith.science/api/pith-number/32UUEQARLAA3EG4KOUYACN7XOF/events.json","paper":"https://pith.science/paper/32UUEQAR"},"agent_actions":{"view_html":"https://pith.science/pith/32UUEQARLAA3EG4KOUYACN7XOF","download_json":"https://pith.science/pith/32UUEQARLAA3EG4KOUYACN7XOF.json","view_paper":"https://pith.science/paper/32UUEQAR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.07251&json=true","fetch_graph":"https://pith.science/api/pith-number/32UUEQARLAA3EG4KOUYACN7XOF/graph.json","fetch_events":"https://pith.science/api/pith-number/32UUEQARLAA3EG4KOUYACN7XOF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/32UUEQARLAA3EG4KOUYACN7XOF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/32UUEQARLAA3EG4KOUYACN7XOF/action/storage_attestation","attest_author":"https://pith.science/pith/32UUEQARLAA3EG4KOUYACN7XOF/action/author_attestation","sign_citation":"https://pith.science/pith/32UUEQARLAA3EG4KOUYACN7XOF/action/citation_signature","submit_replication":"https://pith.science/pith/32UUEQARLAA3EG4KOUYACN7XOF/action/replication_record"}},"created_at":"2026-07-05T11:34:34.213000+00:00","updated_at":"2026-07-05T11:34:34.213000+00:00"}