{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:QSAQJXUL22J7NYDBYAEDKNTNHP","short_pith_number":"pith:QSAQJXUL","schema_version":"1.0","canonical_sha256":"848104de8bd693f6e061c00835366d3be0c89fbd8d8cad03b04cefafe61a88f5","source":{"kind":"arxiv","id":"2309.04250","version":1},"attestation_state":"computed","paper":{"title":"Provider Fairness and Beyond-Accuracy Trade-offs in Recommender Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Hossein A. Rahmani, Leila Safari, Mohammadmehdi Naghiaei, Saeedeh Karimi","submitted_at":"2023-09-08T10:28:41Z","abstract_excerpt":"Recommender systems, while transformative in online user experiences, have raised concerns over potential provider-side fairness issues. These systems may inadvertently favor popular items, thereby marginalizing less popular ones and compromising provider fairness. While previous research has recognized provider-side fairness issues, the investigation into how these biases affect beyond-accuracy aspects of recommendation systems - such as diversity, novelty, coverage, and serendipity - has been less emphasized. In this paper, we address this gap by introducing a simple yet effective post-proce"},"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":"2309.04250","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2023-09-08T10:28:41Z","cross_cats_sorted":[],"title_canon_sha256":"bda2689149d5c13313caa1b3548061d552ac45ef603be7cd467c6e3b448736a2","abstract_canon_sha256":"a9331b3b839ae3dc7731fbb8c306e40ece609135d109a86aa4fd992fd83e88ac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:48:57.219392Z","signature_b64":"isKj6eIEh/xyBKr7IfHWNQaRmRk0I191AKFwdgoE3YQkf1YsW3bRgq18a3zwcHDh8e8pHrzNnYyiWA5Sea13AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"848104de8bd693f6e061c00835366d3be0c89fbd8d8cad03b04cefafe61a88f5","last_reissued_at":"2026-07-05T06:48:57.218906Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:48:57.218906Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Provider Fairness and Beyond-Accuracy Trade-offs in Recommender Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Hossein A. Rahmani, Leila Safari, Mohammadmehdi Naghiaei, Saeedeh Karimi","submitted_at":"2023-09-08T10:28:41Z","abstract_excerpt":"Recommender systems, while transformative in online user experiences, have raised concerns over potential provider-side fairness issues. These systems may inadvertently favor popular items, thereby marginalizing less popular ones and compromising provider fairness. While previous research has recognized provider-side fairness issues, the investigation into how these biases affect beyond-accuracy aspects of recommendation systems - such as diversity, novelty, coverage, and serendipity - has been less emphasized. In this paper, we address this gap by introducing a simple yet effective post-proce"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.04250","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/2309.04250/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":"2309.04250","created_at":"2026-07-05T06:48:57.218954+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.04250v1","created_at":"2026-07-05T06:48:57.218954+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.04250","created_at":"2026-07-05T06:48:57.218954+00:00"},{"alias_kind":"pith_short_12","alias_value":"QSAQJXUL22J7","created_at":"2026-07-05T06:48:57.218954+00:00"},{"alias_kind":"pith_short_16","alias_value":"QSAQJXUL22J7NYDB","created_at":"2026-07-05T06:48:57.218954+00:00"},{"alias_kind":"pith_short_8","alias_value":"QSAQJXUL","created_at":"2026-07-05T06:48:57.218954+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.16419","citing_title":"Modeling User Exploration Saturation: When Recommender Systems Should Stop Pushing Novelty","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25032","citing_title":"Offline Evaluation Measures of Fairness in Recommender Systems","ref_index":119,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QSAQJXUL22J7NYDBYAEDKNTNHP","json":"https://pith.science/pith/QSAQJXUL22J7NYDBYAEDKNTNHP.json","graph_json":"https://pith.science/api/pith-number/QSAQJXUL22J7NYDBYAEDKNTNHP/graph.json","events_json":"https://pith.science/api/pith-number/QSAQJXUL22J7NYDBYAEDKNTNHP/events.json","paper":"https://pith.science/paper/QSAQJXUL"},"agent_actions":{"view_html":"https://pith.science/pith/QSAQJXUL22J7NYDBYAEDKNTNHP","download_json":"https://pith.science/pith/QSAQJXUL22J7NYDBYAEDKNTNHP.json","view_paper":"https://pith.science/paper/QSAQJXUL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.04250&json=true","fetch_graph":"https://pith.science/api/pith-number/QSAQJXUL22J7NYDBYAEDKNTNHP/graph.json","fetch_events":"https://pith.science/api/pith-number/QSAQJXUL22J7NYDBYAEDKNTNHP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QSAQJXUL22J7NYDBYAEDKNTNHP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QSAQJXUL22J7NYDBYAEDKNTNHP/action/storage_attestation","attest_author":"https://pith.science/pith/QSAQJXUL22J7NYDBYAEDKNTNHP/action/author_attestation","sign_citation":"https://pith.science/pith/QSAQJXUL22J7NYDBYAEDKNTNHP/action/citation_signature","submit_replication":"https://pith.science/pith/QSAQJXUL22J7NYDBYAEDKNTNHP/action/replication_record"}},"created_at":"2026-07-05T06:48:57.218954+00:00","updated_at":"2026-07-05T06:48:57.218954+00:00"}