{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:TDEZCBMLUZL2CWWZEZ2LKR7W22","short_pith_number":"pith:TDEZCBML","schema_version":"1.0","canonical_sha256":"98c991058ba657a15ad92674b547f6d69e0ab276e11b2665d9ae287e4b90f1ab","source":{"kind":"arxiv","id":"1812.04109","version":2},"attestation_state":"computed","paper":{"title":"Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.IR","authors_text":"Jinlong Hu, Junjie Liang, Shoubin Dong, Vasant Honavar","submitted_at":"2018-12-10T21:39:16Z","abstract_excerpt":"We propose Top-N-Rank, a novel family of list-wise Learning-to-Rank models for reliably recommending the N top-ranked items. The proposed models optimize a variant of the widely used discounted cumulative gain (DCG) objective function which differs from DCG in two important aspects: (i) It limits the evaluation of DCG only on the top N items in the ranked lists, thereby eliminating the impact of low-ranked items on the learned ranking function; and (ii) it incorporates weights that allow the model to leverage multiple types of implicit feedback with differing levels of reliability or trustwort"},"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":"1812.04109","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-12-10T21:39:16Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"e9d81d3750c6d9f133b7b45fdb3164e238ac07f8b2d23f180594bceed710b63f","abstract_canon_sha256":"c7aac4ced17524a14dc999494ed4947a2d84b40107b2436c4cf4c4324a2fd94b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:56.771756Z","signature_b64":"ndHa41JJqanUqeNOS+2Eg3NrMPaxfWDa2SiCTB6ZPtcjdkT6eYakK7N35uPAVQSw9iN0LtyVJ2VOSmV3++w1Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98c991058ba657a15ad92674b547f6d69e0ab276e11b2665d9ae287e4b90f1ab","last_reissued_at":"2026-05-17T23:57:56.771020Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:56.771020Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.IR","authors_text":"Jinlong Hu, Junjie Liang, Shoubin Dong, Vasant Honavar","submitted_at":"2018-12-10T21:39:16Z","abstract_excerpt":"We propose Top-N-Rank, a novel family of list-wise Learning-to-Rank models for reliably recommending the N top-ranked items. The proposed models optimize a variant of the widely used discounted cumulative gain (DCG) objective function which differs from DCG in two important aspects: (i) It limits the evaluation of DCG only on the top N items in the ranked lists, thereby eliminating the impact of low-ranked items on the learned ranking function; and (ii) it incorporates weights that allow the model to leverage multiple types of implicit feedback with differing levels of reliability or trustwort"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.04109","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":""},"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":"1812.04109","created_at":"2026-05-17T23:57:56.771145+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.04109v2","created_at":"2026-05-17T23:57:56.771145+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.04109","created_at":"2026-05-17T23:57:56.771145+00:00"},{"alias_kind":"pith_short_12","alias_value":"TDEZCBMLUZL2","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"TDEZCBMLUZL2CWWZ","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"TDEZCBML","created_at":"2026-05-18T12:32:53.628368+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/TDEZCBMLUZL2CWWZEZ2LKR7W22","json":"https://pith.science/pith/TDEZCBMLUZL2CWWZEZ2LKR7W22.json","graph_json":"https://pith.science/api/pith-number/TDEZCBMLUZL2CWWZEZ2LKR7W22/graph.json","events_json":"https://pith.science/api/pith-number/TDEZCBMLUZL2CWWZEZ2LKR7W22/events.json","paper":"https://pith.science/paper/TDEZCBML"},"agent_actions":{"view_html":"https://pith.science/pith/TDEZCBMLUZL2CWWZEZ2LKR7W22","download_json":"https://pith.science/pith/TDEZCBMLUZL2CWWZEZ2LKR7W22.json","view_paper":"https://pith.science/paper/TDEZCBML","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.04109&json=true","fetch_graph":"https://pith.science/api/pith-number/TDEZCBMLUZL2CWWZEZ2LKR7W22/graph.json","fetch_events":"https://pith.science/api/pith-number/TDEZCBMLUZL2CWWZEZ2LKR7W22/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TDEZCBMLUZL2CWWZEZ2LKR7W22/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TDEZCBMLUZL2CWWZEZ2LKR7W22/action/storage_attestation","attest_author":"https://pith.science/pith/TDEZCBMLUZL2CWWZEZ2LKR7W22/action/author_attestation","sign_citation":"https://pith.science/pith/TDEZCBMLUZL2CWWZEZ2LKR7W22/action/citation_signature","submit_replication":"https://pith.science/pith/TDEZCBMLUZL2CWWZEZ2LKR7W22/action/replication_record"}},"created_at":"2026-05-17T23:57:56.771145+00:00","updated_at":"2026-05-17T23:57:56.771145+00:00"}