{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:TMTOSNSHB5NZHGMQT2VQTGV26H","short_pith_number":"pith:TMTOSNSH","schema_version":"1.0","canonical_sha256":"9b26e936470f5b9399909eab099abaf1e0d5b77b2b90b597745d9121a38788e9","source":{"kind":"arxiv","id":"2110.08353","version":1},"attestation_state":"computed","paper":{"title":"Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.IR","authors_text":"Bhaskar Mitra, Catherine Stinson, Nicola Neophytou","submitted_at":"2021-10-15T20:30:51Z","abstract_excerpt":"Recommendation algorithms are susceptible to popularity bias: a tendency to recommend popular items even when they fail to meet user needs. A related issue is that the recommendation quality can vary by demographic groups. Marginalized groups or groups that are under-represented in the training data may receive less relevant recommendations from these algorithms compared to others. In a recent study, Ekstrand et al. investigate how recommender performance varies according to popularity and demographics, and find statistically significant differences in recommendation utility between binary gen"},"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":"2110.08353","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2021-10-15T20:30:51Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"0baae89a2ab064cbbc4908c3774272a9fb668f1d2e7464367822b8e4994ddea6","abstract_canon_sha256":"166d16ad6bd88ce7e243af4875fd3bc8f067f8d0ea9f642d39817958993b70d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:23:16.539686Z","signature_b64":"i+pKlB4hiZKAmj1My/Vt77dg4nEJ3oJDscp0WmQY0flTS36sZAgJd8bV7rVnX1QmndMg8Bys7wRsKKOeAt2mCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b26e936470f5b9399909eab099abaf1e0d5b77b2b90b597745d9121a38788e9","last_reissued_at":"2026-07-05T03:23:16.539363Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:23:16.539363Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.IR","authors_text":"Bhaskar Mitra, Catherine Stinson, Nicola Neophytou","submitted_at":"2021-10-15T20:30:51Z","abstract_excerpt":"Recommendation algorithms are susceptible to popularity bias: a tendency to recommend popular items even when they fail to meet user needs. A related issue is that the recommendation quality can vary by demographic groups. Marginalized groups or groups that are under-represented in the training data may receive less relevant recommendations from these algorithms compared to others. In a recent study, Ekstrand et al. investigate how recommender performance varies according to popularity and demographics, and find statistically significant differences in recommendation utility between binary gen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.08353","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/2110.08353/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":"2110.08353","created_at":"2026-07-05T03:23:16.539413+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.08353v1","created_at":"2026-07-05T03:23:16.539413+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.08353","created_at":"2026-07-05T03:23:16.539413+00:00"},{"alias_kind":"pith_short_12","alias_value":"TMTOSNSHB5NZ","created_at":"2026-07-05T03:23:16.539413+00:00"},{"alias_kind":"pith_short_16","alias_value":"TMTOSNSHB5NZHGMQ","created_at":"2026-07-05T03:23:16.539413+00:00"},{"alias_kind":"pith_short_8","alias_value":"TMTOSNSH","created_at":"2026-07-05T03:23:16.539413+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/TMTOSNSHB5NZHGMQT2VQTGV26H","json":"https://pith.science/pith/TMTOSNSHB5NZHGMQT2VQTGV26H.json","graph_json":"https://pith.science/api/pith-number/TMTOSNSHB5NZHGMQT2VQTGV26H/graph.json","events_json":"https://pith.science/api/pith-number/TMTOSNSHB5NZHGMQT2VQTGV26H/events.json","paper":"https://pith.science/paper/TMTOSNSH"},"agent_actions":{"view_html":"https://pith.science/pith/TMTOSNSHB5NZHGMQT2VQTGV26H","download_json":"https://pith.science/pith/TMTOSNSHB5NZHGMQT2VQTGV26H.json","view_paper":"https://pith.science/paper/TMTOSNSH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.08353&json=true","fetch_graph":"https://pith.science/api/pith-number/TMTOSNSHB5NZHGMQT2VQTGV26H/graph.json","fetch_events":"https://pith.science/api/pith-number/TMTOSNSHB5NZHGMQT2VQTGV26H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TMTOSNSHB5NZHGMQT2VQTGV26H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TMTOSNSHB5NZHGMQT2VQTGV26H/action/storage_attestation","attest_author":"https://pith.science/pith/TMTOSNSHB5NZHGMQT2VQTGV26H/action/author_attestation","sign_citation":"https://pith.science/pith/TMTOSNSHB5NZHGMQT2VQTGV26H/action/citation_signature","submit_replication":"https://pith.science/pith/TMTOSNSHB5NZHGMQT2VQTGV26H/action/replication_record"}},"created_at":"2026-07-05T03:23:16.539413+00:00","updated_at":"2026-07-05T03:23:16.539413+00:00"}