{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:A5QUSQK3MZMPCHBPX7KKPGVAQ3","short_pith_number":"pith:A5QUSQK3","schema_version":"1.0","canonical_sha256":"076149415b6658f11c2fbfd4a79aa086ea8dd6f279cdea968e6fe96694a624cb","source":{"kind":"arxiv","id":"2401.10545","version":3},"attestation_state":"computed","paper":{"title":"Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Yashar Deldjoo","submitted_at":"2024-01-19T08:09:20Z","abstract_excerpt":"This paper explores the biases in ChatGPT-based recommender systems, focusing on provider fairness (item-side fairness). Through extensive experiments and over a thousand API calls, we investigate the impact of prompt design strategies-including structure, system role, and intent-on evaluation metrics such as provider fairness, catalog coverage, temporal stability, and recency. The first experiment examines these strategies in classical top-K recommendations, while the second evaluates sequential in-context learning (ICL).\n  In the first experiment, we assess seven distinct prompt scenarios on"},"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":"2401.10545","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-01-19T08:09:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"714d165c19be5537c8593b95fe144b7d237fd4c0e5472afae82deba0e53321e5","abstract_canon_sha256":"8cd8be3e6f060e8d57dc859680f0b004812749b3f4c1e46e256da0aaf420a4f2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:40:12.494958Z","signature_b64":"S7prrfnvpND2nHZctPk+0mZEXIgUC9G7qtKN8Sq4Sg2+1bPBiPVqvt2JCKI14tFnPo2i/Vw6D37Rmxx9oJ9aCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"076149415b6658f11c2fbfd4a79aa086ea8dd6f279cdea968e6fe96694a624cb","last_reissued_at":"2026-07-05T08:40:12.494531Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:40:12.494531Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Yashar Deldjoo","submitted_at":"2024-01-19T08:09:20Z","abstract_excerpt":"This paper explores the biases in ChatGPT-based recommender systems, focusing on provider fairness (item-side fairness). Through extensive experiments and over a thousand API calls, we investigate the impact of prompt design strategies-including structure, system role, and intent-on evaluation metrics such as provider fairness, catalog coverage, temporal stability, and recency. The first experiment examines these strategies in classical top-K recommendations, while the second evaluates sequential in-context learning (ICL).\n  In the first experiment, we assess seven distinct prompt scenarios on"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.10545","kind":"arxiv","version":3},"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/2401.10545/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":"2401.10545","created_at":"2026-07-05T08:40:12.494586+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.10545v3","created_at":"2026-07-05T08:40:12.494586+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.10545","created_at":"2026-07-05T08:40:12.494586+00:00"},{"alias_kind":"pith_short_12","alias_value":"A5QUSQK3MZMP","created_at":"2026-07-05T08:40:12.494586+00:00"},{"alias_kind":"pith_short_16","alias_value":"A5QUSQK3MZMPCHBP","created_at":"2026-07-05T08:40:12.494586+00:00"},{"alias_kind":"pith_short_8","alias_value":"A5QUSQK3","created_at":"2026-07-05T08:40:12.494586+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/A5QUSQK3MZMPCHBPX7KKPGVAQ3","json":"https://pith.science/pith/A5QUSQK3MZMPCHBPX7KKPGVAQ3.json","graph_json":"https://pith.science/api/pith-number/A5QUSQK3MZMPCHBPX7KKPGVAQ3/graph.json","events_json":"https://pith.science/api/pith-number/A5QUSQK3MZMPCHBPX7KKPGVAQ3/events.json","paper":"https://pith.science/paper/A5QUSQK3"},"agent_actions":{"view_html":"https://pith.science/pith/A5QUSQK3MZMPCHBPX7KKPGVAQ3","download_json":"https://pith.science/pith/A5QUSQK3MZMPCHBPX7KKPGVAQ3.json","view_paper":"https://pith.science/paper/A5QUSQK3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.10545&json=true","fetch_graph":"https://pith.science/api/pith-number/A5QUSQK3MZMPCHBPX7KKPGVAQ3/graph.json","fetch_events":"https://pith.science/api/pith-number/A5QUSQK3MZMPCHBPX7KKPGVAQ3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A5QUSQK3MZMPCHBPX7KKPGVAQ3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A5QUSQK3MZMPCHBPX7KKPGVAQ3/action/storage_attestation","attest_author":"https://pith.science/pith/A5QUSQK3MZMPCHBPX7KKPGVAQ3/action/author_attestation","sign_citation":"https://pith.science/pith/A5QUSQK3MZMPCHBPX7KKPGVAQ3/action/citation_signature","submit_replication":"https://pith.science/pith/A5QUSQK3MZMPCHBPX7KKPGVAQ3/action/replication_record"}},"created_at":"2026-07-05T08:40:12.494586+00:00","updated_at":"2026-07-05T08:40:12.494586+00:00"}