{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:M2HDGHKFPL2HQU77SVQUZJS2R2","short_pith_number":"pith:M2HDGHKF","schema_version":"1.0","canonical_sha256":"668e331d457af47853ff95614ca65a8e9c0044e9fa100e5730ac0d6ee94f4f45","source":{"kind":"arxiv","id":"1706.04453","version":2},"attestation_state":"computed","paper":{"title":"Hybrid Collaborative Recommendation via Semi-AutoEncoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Liming Zhu, Lina Yao, Sen Wang, Shuai Zhang, Xiwei Xu","submitted_at":"2017-06-14T12:47:36Z","abstract_excerpt":"In this paper, we present a novel structure, Semi-AutoEncoder, based on AutoEncoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as personalized top-n recommendations. Experimental results on two real-world datasets demonstrate its state-of-the-art performances."},"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":"1706.04453","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-06-14T12:47:36Z","cross_cats_sorted":[],"title_canon_sha256":"62752b2cc7f495e81a95eae4382830c02c910478d44c62e2ba9747040b454fcf","abstract_canon_sha256":"18e851bacc776af7bbcf96117725ee9a126ed4a43aa3a70f8722827e18d2c649"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:37:58.453613Z","signature_b64":"/xyDij0TUe5guapmh27lFaEDUbw5Q4E5QSDn6mXJIp0tA/fHQ2dnl6NQXlAwSCQC87hj111Hug/1DA2BkBJXCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"668e331d457af47853ff95614ca65a8e9c0044e9fa100e5730ac0d6ee94f4f45","last_reissued_at":"2026-05-18T00:37:58.453202Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:37:58.453202Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hybrid Collaborative Recommendation via Semi-AutoEncoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Liming Zhu, Lina Yao, Sen Wang, Shuai Zhang, Xiwei Xu","submitted_at":"2017-06-14T12:47:36Z","abstract_excerpt":"In this paper, we present a novel structure, Semi-AutoEncoder, based on AutoEncoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as personalized top-n recommendations. Experimental results on two real-world datasets demonstrate its state-of-the-art performances."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.04453","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":"1706.04453","created_at":"2026-05-18T00:37:58.453262+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.04453v2","created_at":"2026-05-18T00:37:58.453262+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.04453","created_at":"2026-05-18T00:37:58.453262+00:00"},{"alias_kind":"pith_short_12","alias_value":"M2HDGHKFPL2H","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"M2HDGHKFPL2HQU77","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"M2HDGHKF","created_at":"2026-05-18T12:31:28.150371+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/M2HDGHKFPL2HQU77SVQUZJS2R2","json":"https://pith.science/pith/M2HDGHKFPL2HQU77SVQUZJS2R2.json","graph_json":"https://pith.science/api/pith-number/M2HDGHKFPL2HQU77SVQUZJS2R2/graph.json","events_json":"https://pith.science/api/pith-number/M2HDGHKFPL2HQU77SVQUZJS2R2/events.json","paper":"https://pith.science/paper/M2HDGHKF"},"agent_actions":{"view_html":"https://pith.science/pith/M2HDGHKFPL2HQU77SVQUZJS2R2","download_json":"https://pith.science/pith/M2HDGHKFPL2HQU77SVQUZJS2R2.json","view_paper":"https://pith.science/paper/M2HDGHKF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.04453&json=true","fetch_graph":"https://pith.science/api/pith-number/M2HDGHKFPL2HQU77SVQUZJS2R2/graph.json","fetch_events":"https://pith.science/api/pith-number/M2HDGHKFPL2HQU77SVQUZJS2R2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M2HDGHKFPL2HQU77SVQUZJS2R2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M2HDGHKFPL2HQU77SVQUZJS2R2/action/storage_attestation","attest_author":"https://pith.science/pith/M2HDGHKFPL2HQU77SVQUZJS2R2/action/author_attestation","sign_citation":"https://pith.science/pith/M2HDGHKFPL2HQU77SVQUZJS2R2/action/citation_signature","submit_replication":"https://pith.science/pith/M2HDGHKFPL2HQU77SVQUZJS2R2/action/replication_record"}},"created_at":"2026-05-18T00:37:58.453262+00:00","updated_at":"2026-05-18T00:37:58.453262+00:00"}