{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:HVICYUR6NGDW5OUE5RAK3PWA7Y","short_pith_number":"pith:HVICYUR6","schema_version":"1.0","canonical_sha256":"3d502c523e69876eba84ec40adbec0fe2aafa8b717b39fda6cf7475a5c83ebc0","source":{"kind":"arxiv","id":"1802.08988","version":1},"attestation_state":"computed","paper":{"title":"Deep Neural Network for Learning to Rank Query-Text Pairs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Baoyang Song","submitted_at":"2018-02-25T11:15:31Z","abstract_excerpt":"This paper considers the problem of document ranking in information retrieval systems by Learning to Rank. We propose ConvRankNet combining a Siamese Convolutional Neural Network encoder and the RankNet ranking model which could be trained in an end-to-end fashion. We prove a general result justifying the linear test-time complexity of pairwise Learning to Rank approach. Experiments on the OHSUMED dataset show that ConvRankNet outperforms systematically existing feature-based models."},"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":"1802.08988","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-02-25T11:15:31Z","cross_cats_sorted":[],"title_canon_sha256":"965acd2cd72dbfbce8df6292b8167bc289321f3c5daac6c9e974cc4ad6d28aaa","abstract_canon_sha256":"aaa4e83a83789f85807f0dd74c58f1318601f7d43ab3a9f20d399d7996965deb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:35.845034Z","signature_b64":"MBZ11tiYQhFOBnZRtjWGSt0cirhL30TUIMwdMWan5oZS9NlhBZ2pPe6L5bjescFR1S4T2RIhkHwwGuLDKcdHDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d502c523e69876eba84ec40adbec0fe2aafa8b717b39fda6cf7475a5c83ebc0","last_reissued_at":"2026-05-18T00:22:35.844432Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:35.844432Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Neural Network for Learning to Rank Query-Text Pairs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Baoyang Song","submitted_at":"2018-02-25T11:15:31Z","abstract_excerpt":"This paper considers the problem of document ranking in information retrieval systems by Learning to Rank. We propose ConvRankNet combining a Siamese Convolutional Neural Network encoder and the RankNet ranking model which could be trained in an end-to-end fashion. We prove a general result justifying the linear test-time complexity of pairwise Learning to Rank approach. Experiments on the OHSUMED dataset show that ConvRankNet outperforms systematically existing feature-based models."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.08988","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":""},"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":"1802.08988","created_at":"2026-05-18T00:22:35.844521+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.08988v1","created_at":"2026-05-18T00:22:35.844521+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.08988","created_at":"2026-05-18T00:22:35.844521+00:00"},{"alias_kind":"pith_short_12","alias_value":"HVICYUR6NGDW","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"HVICYUR6NGDW5OUE","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"HVICYUR6","created_at":"2026-05-18T12:32:28.185984+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/HVICYUR6NGDW5OUE5RAK3PWA7Y","json":"https://pith.science/pith/HVICYUR6NGDW5OUE5RAK3PWA7Y.json","graph_json":"https://pith.science/api/pith-number/HVICYUR6NGDW5OUE5RAK3PWA7Y/graph.json","events_json":"https://pith.science/api/pith-number/HVICYUR6NGDW5OUE5RAK3PWA7Y/events.json","paper":"https://pith.science/paper/HVICYUR6"},"agent_actions":{"view_html":"https://pith.science/pith/HVICYUR6NGDW5OUE5RAK3PWA7Y","download_json":"https://pith.science/pith/HVICYUR6NGDW5OUE5RAK3PWA7Y.json","view_paper":"https://pith.science/paper/HVICYUR6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.08988&json=true","fetch_graph":"https://pith.science/api/pith-number/HVICYUR6NGDW5OUE5RAK3PWA7Y/graph.json","fetch_events":"https://pith.science/api/pith-number/HVICYUR6NGDW5OUE5RAK3PWA7Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HVICYUR6NGDW5OUE5RAK3PWA7Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HVICYUR6NGDW5OUE5RAK3PWA7Y/action/storage_attestation","attest_author":"https://pith.science/pith/HVICYUR6NGDW5OUE5RAK3PWA7Y/action/author_attestation","sign_citation":"https://pith.science/pith/HVICYUR6NGDW5OUE5RAK3PWA7Y/action/citation_signature","submit_replication":"https://pith.science/pith/HVICYUR6NGDW5OUE5RAK3PWA7Y/action/replication_record"}},"created_at":"2026-05-18T00:22:35.844521+00:00","updated_at":"2026-05-18T00:22:35.844521+00:00"}