{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5KTF4ADPWUW7KFFRCT7O6CJCQR","short_pith_number":"pith:5KTF4ADP","schema_version":"1.0","canonical_sha256":"eaa65e006fb52df514b114feef0922846b58ecad63ab97d6e317bb0d5373f46d","source":{"kind":"arxiv","id":"2310.11777","version":1},"attestation_state":"computed","paper":{"title":"DCRNN: A Deep Cross approach based on RNN for Partial Parameter Sharing in Multi-task Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Jie Zhou, Qian Yu","submitted_at":"2023-10-18T08:16:27Z","abstract_excerpt":"In recent years, DL has developed rapidly, and personalized services are exploring using DL algorithms to improve the performance of the recommendation system. For personalized services, a successful recommendation consists of two parts: attracting users to click the item and users being willing to consume the item. If both tasks need to be predicted at the same time, traditional recommendation systems generally train two independent models. This approach is cumbersome and does not effectively model the relationship between the two subtasks of \"click-consumption\". Therefore, in order to improv"},"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":"2310.11777","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-10-18T08:16:27Z","cross_cats_sorted":[],"title_canon_sha256":"4daa74873b798cd752a5e8697c9a83e8b9880cb31b8bb17fe9071a5480d91fe1","abstract_canon_sha256":"b61b90e4ebe713fa4e78c8d686e9bdce904a68ae6efd83805a8bc522cd36180b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:02:12.044130Z","signature_b64":"Wt+kt+FqULCrAUxftFeYkXkO2rrmta252uaHK1cYe++yuKcuTld23uTIBwJWbNGI9ji4RC3CoOB1vGqOSjD5Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eaa65e006fb52df514b114feef0922846b58ecad63ab97d6e317bb0d5373f46d","last_reissued_at":"2026-07-05T07:02:12.043687Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:02:12.043687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DCRNN: A Deep Cross approach based on RNN for Partial Parameter Sharing in Multi-task Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Jie Zhou, Qian Yu","submitted_at":"2023-10-18T08:16:27Z","abstract_excerpt":"In recent years, DL has developed rapidly, and personalized services are exploring using DL algorithms to improve the performance of the recommendation system. For personalized services, a successful recommendation consists of two parts: attracting users to click the item and users being willing to consume the item. If both tasks need to be predicted at the same time, traditional recommendation systems generally train two independent models. This approach is cumbersome and does not effectively model the relationship between the two subtasks of \"click-consumption\". Therefore, in order to improv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.11777","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/2310.11777/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":"2310.11777","created_at":"2026-07-05T07:02:12.043744+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.11777v1","created_at":"2026-07-05T07:02:12.043744+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.11777","created_at":"2026-07-05T07:02:12.043744+00:00"},{"alias_kind":"pith_short_12","alias_value":"5KTF4ADPWUW7","created_at":"2026-07-05T07:02:12.043744+00:00"},{"alias_kind":"pith_short_16","alias_value":"5KTF4ADPWUW7KFFR","created_at":"2026-07-05T07:02:12.043744+00:00"},{"alias_kind":"pith_short_8","alias_value":"5KTF4ADP","created_at":"2026-07-05T07:02:12.043744+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.07674","citing_title":"Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5KTF4ADPWUW7KFFRCT7O6CJCQR","json":"https://pith.science/pith/5KTF4ADPWUW7KFFRCT7O6CJCQR.json","graph_json":"https://pith.science/api/pith-number/5KTF4ADPWUW7KFFRCT7O6CJCQR/graph.json","events_json":"https://pith.science/api/pith-number/5KTF4ADPWUW7KFFRCT7O6CJCQR/events.json","paper":"https://pith.science/paper/5KTF4ADP"},"agent_actions":{"view_html":"https://pith.science/pith/5KTF4ADPWUW7KFFRCT7O6CJCQR","download_json":"https://pith.science/pith/5KTF4ADPWUW7KFFRCT7O6CJCQR.json","view_paper":"https://pith.science/paper/5KTF4ADP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.11777&json=true","fetch_graph":"https://pith.science/api/pith-number/5KTF4ADPWUW7KFFRCT7O6CJCQR/graph.json","fetch_events":"https://pith.science/api/pith-number/5KTF4ADPWUW7KFFRCT7O6CJCQR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5KTF4ADPWUW7KFFRCT7O6CJCQR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5KTF4ADPWUW7KFFRCT7O6CJCQR/action/storage_attestation","attest_author":"https://pith.science/pith/5KTF4ADPWUW7KFFRCT7O6CJCQR/action/author_attestation","sign_citation":"https://pith.science/pith/5KTF4ADPWUW7KFFRCT7O6CJCQR/action/citation_signature","submit_replication":"https://pith.science/pith/5KTF4ADPWUW7KFFRCT7O6CJCQR/action/replication_record"}},"created_at":"2026-07-05T07:02:12.043744+00:00","updated_at":"2026-07-05T07:02:12.043744+00:00"}