{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:AMLAHGWDECGDJWZLLJKH7A2ESG","short_pith_number":"pith:AMLAHGWD","schema_version":"1.0","canonical_sha256":"0316039ac3208c34db2b5a547f834491b26276fd478f4d2f80b34adb93d9f594","source":{"kind":"arxiv","id":"1805.06563","version":1},"attestation_state":"computed","paper":{"title":"NPE: Neural Personalized Embedding for Collaborative Filtering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Atsuhiro Takasu, ThaiBinh Nguyen","submitted_at":"2018-05-17T00:57:26Z","abstract_excerpt":"Matrix factorization is one of the most efficient approaches in recommender systems. However, such algorithms, which rely on the interactions between users and items, perform poorly for \"cold-users\" (users with little history of such interactions) and at capturing the relationships between closely related items. To address these problems, we propose a neural personalized embedding (NPE) model, which improves the recommendation performance for cold-users and can learn effective representations of items. It models a user's click to an item in two terms: the personal preference of the user for th"},"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":"1805.06563","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-05-17T00:57:26Z","cross_cats_sorted":[],"title_canon_sha256":"c157a9d589f1969a5c0e8c3aa86d09f3b6fafd3bd39aac0b8bb3f744a7269213","abstract_canon_sha256":"1055595d914fa9cde040f8b4d47df59fe7e037a94192279b82ebfba98db4c93f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:44.049958Z","signature_b64":"DqhtrIO9ElDl2xtSjNi4fW51jWM4fcPcKC4r+EMaa/6f2+SGmBZZA0iVOoPIWzyAr42TL+6mrXtU5dxGWptHDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0316039ac3208c34db2b5a547f834491b26276fd478f4d2f80b34adb93d9f594","last_reissued_at":"2026-05-18T00:15:44.049357Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:44.049357Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NPE: Neural Personalized Embedding for Collaborative Filtering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Atsuhiro Takasu, ThaiBinh Nguyen","submitted_at":"2018-05-17T00:57:26Z","abstract_excerpt":"Matrix factorization is one of the most efficient approaches in recommender systems. However, such algorithms, which rely on the interactions between users and items, perform poorly for \"cold-users\" (users with little history of such interactions) and at capturing the relationships between closely related items. To address these problems, we propose a neural personalized embedding (NPE) model, which improves the recommendation performance for cold-users and can learn effective representations of items. It models a user's click to an item in two terms: the personal preference of the user for th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.06563","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":"1805.06563","created_at":"2026-05-18T00:15:44.049462+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.06563v1","created_at":"2026-05-18T00:15:44.049462+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.06563","created_at":"2026-05-18T00:15:44.049462+00:00"},{"alias_kind":"pith_short_12","alias_value":"AMLAHGWDECGD","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"AMLAHGWDECGDJWZL","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"AMLAHGWD","created_at":"2026-05-18T12:32:13.499390+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/AMLAHGWDECGDJWZLLJKH7A2ESG","json":"https://pith.science/pith/AMLAHGWDECGDJWZLLJKH7A2ESG.json","graph_json":"https://pith.science/api/pith-number/AMLAHGWDECGDJWZLLJKH7A2ESG/graph.json","events_json":"https://pith.science/api/pith-number/AMLAHGWDECGDJWZLLJKH7A2ESG/events.json","paper":"https://pith.science/paper/AMLAHGWD"},"agent_actions":{"view_html":"https://pith.science/pith/AMLAHGWDECGDJWZLLJKH7A2ESG","download_json":"https://pith.science/pith/AMLAHGWDECGDJWZLLJKH7A2ESG.json","view_paper":"https://pith.science/paper/AMLAHGWD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.06563&json=true","fetch_graph":"https://pith.science/api/pith-number/AMLAHGWDECGDJWZLLJKH7A2ESG/graph.json","fetch_events":"https://pith.science/api/pith-number/AMLAHGWDECGDJWZLLJKH7A2ESG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AMLAHGWDECGDJWZLLJKH7A2ESG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AMLAHGWDECGDJWZLLJKH7A2ESG/action/storage_attestation","attest_author":"https://pith.science/pith/AMLAHGWDECGDJWZLLJKH7A2ESG/action/author_attestation","sign_citation":"https://pith.science/pith/AMLAHGWDECGDJWZLLJKH7A2ESG/action/citation_signature","submit_replication":"https://pith.science/pith/AMLAHGWDECGDJWZLLJKH7A2ESG/action/replication_record"}},"created_at":"2026-05-18T00:15:44.049462+00:00","updated_at":"2026-05-18T00:15:44.049462+00:00"}