{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:5AY2IT4VZSO6G4AQDNCVFYAIXU","short_pith_number":"pith:5AY2IT4V","schema_version":"1.0","canonical_sha256":"e831a44f95cc9de370101b4552e008bd357f99d710f1bab460726f597aa96260","source":{"kind":"arxiv","id":"1905.02681","version":1},"attestation_state":"computed","paper":{"title":"A general graph-based framework for top-N recommendation using content, temporal and trust information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.IR","authors_text":"Armel Jacques Nzekon Nzeko'o, Matthieu Latapy, Maurice Tchuente","submitted_at":"2019-05-06T12:52:44Z","abstract_excerpt":"Recommending appropriate items to users is crucial in many e-commerce platforms that contain implicit data as users' browsing, purchasing and streaming history. One common approach consists in selecting the N most relevant items to each user, for a given N, which is called top-N recommendation. To do so, recommender systems rely on various kinds of information, like item and user features, past interest of users for items, browsing history and trust between users. However, they often use only one or two such pieces of information, which limits their performance. In this paper, we design and im"},"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":"1905.02681","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-05-06T12:52:44Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"1b0f3d3add5f8fac079dd67604a5c904381ab7884f57f1e7c6c72ad7dfbe88a6","abstract_canon_sha256":"4cdabc41052e31e5b132d0998c8d1174ed0f00451286e42dd1918a18833496d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:19.334218Z","signature_b64":"HZWGZJtCigxRXQ2kMuiDw3ICJ/rwAzCasZ+9XPPDxH4+3tlnnBDo/ZOD/U0DzGBIzPo3H9tNjLK6C/xhcixeAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e831a44f95cc9de370101b4552e008bd357f99d710f1bab460726f597aa96260","last_reissued_at":"2026-05-17T23:42:19.333746Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:19.333746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A general graph-based framework for top-N recommendation using content, temporal and trust information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.IR","authors_text":"Armel Jacques Nzekon Nzeko'o, Matthieu Latapy, Maurice Tchuente","submitted_at":"2019-05-06T12:52:44Z","abstract_excerpt":"Recommending appropriate items to users is crucial in many e-commerce platforms that contain implicit data as users' browsing, purchasing and streaming history. One common approach consists in selecting the N most relevant items to each user, for a given N, which is called top-N recommendation. To do so, recommender systems rely on various kinds of information, like item and user features, past interest of users for items, browsing history and trust between users. However, they often use only one or two such pieces of information, which limits their performance. In this paper, we design and im"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.02681","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":"1905.02681","created_at":"2026-05-17T23:42:19.333817+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.02681v1","created_at":"2026-05-17T23:42:19.333817+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.02681","created_at":"2026-05-17T23:42:19.333817+00:00"},{"alias_kind":"pith_short_12","alias_value":"5AY2IT4VZSO6","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"5AY2IT4VZSO6G4AQ","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"5AY2IT4V","created_at":"2026-05-18T12:33:10.108867+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/5AY2IT4VZSO6G4AQDNCVFYAIXU","json":"https://pith.science/pith/5AY2IT4VZSO6G4AQDNCVFYAIXU.json","graph_json":"https://pith.science/api/pith-number/5AY2IT4VZSO6G4AQDNCVFYAIXU/graph.json","events_json":"https://pith.science/api/pith-number/5AY2IT4VZSO6G4AQDNCVFYAIXU/events.json","paper":"https://pith.science/paper/5AY2IT4V"},"agent_actions":{"view_html":"https://pith.science/pith/5AY2IT4VZSO6G4AQDNCVFYAIXU","download_json":"https://pith.science/pith/5AY2IT4VZSO6G4AQDNCVFYAIXU.json","view_paper":"https://pith.science/paper/5AY2IT4V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.02681&json=true","fetch_graph":"https://pith.science/api/pith-number/5AY2IT4VZSO6G4AQDNCVFYAIXU/graph.json","fetch_events":"https://pith.science/api/pith-number/5AY2IT4VZSO6G4AQDNCVFYAIXU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5AY2IT4VZSO6G4AQDNCVFYAIXU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5AY2IT4VZSO6G4AQDNCVFYAIXU/action/storage_attestation","attest_author":"https://pith.science/pith/5AY2IT4VZSO6G4AQDNCVFYAIXU/action/author_attestation","sign_citation":"https://pith.science/pith/5AY2IT4VZSO6G4AQDNCVFYAIXU/action/citation_signature","submit_replication":"https://pith.science/pith/5AY2IT4VZSO6G4AQDNCVFYAIXU/action/replication_record"}},"created_at":"2026-05-17T23:42:19.333817+00:00","updated_at":"2026-05-17T23:42:19.333817+00:00"}