{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:D3IQY54HXHRTA55CLZR75Y7N2F","short_pith_number":"pith:D3IQY54H","schema_version":"1.0","canonical_sha256":"1ed10c7787b9e33077a25e63fee3edd178e7f572509d7189247e2f59cbd63c57","source":{"kind":"arxiv","id":"2009.04104","version":1},"attestation_state":"computed","paper":{"title":"Rule-Guided Graph Neural Networks for Recommender Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.LG","authors_text":"Guangyao Li, Jiacheng Huang, Wei Hu, Xinze Lyu","submitted_at":"2020-09-09T05:00:02Z","abstract_excerpt":"To alleviate the cold start problem caused by collaborative filtering in recommender systems, knowledge graphs (KGs) are increasingly employed by many methods as auxiliary resources. However, existing work incorporated with KGs cannot capture the explicit long-range semantics between users and items meanwhile consider various connectivity between items. In this paper, we propose RGRec, which combines rule learning and graph neural networks (GNNs) for recommendation. RGRec first maps items to corresponding entities in KGs and adds users as new entities. Then, it automatically learns rules to mo"},"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":"2009.04104","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-09T05:00:02Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"f3368d21727bd7cb54d51901a6b1597ff7a21fcefc4613c5e3e9f7ff43b0b6ba","abstract_canon_sha256":"b9513a68cb4015bb6af625097c58031bc602e5fb4258de851281c4eed03ee35c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:34:14.852721Z","signature_b64":"ZZ3dm2MgIZsMDXywFo1lg+xKE3nYYgtv9oyfDyIYRNtl8DrwSFF4JjVwGTol7xpdVn7AKR3J2HxoOFWvkzVIAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ed10c7787b9e33077a25e63fee3edd178e7f572509d7189247e2f59cbd63c57","last_reissued_at":"2026-07-05T01:34:14.852312Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:34:14.852312Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rule-Guided Graph Neural Networks for Recommender Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.LG","authors_text":"Guangyao Li, Jiacheng Huang, Wei Hu, Xinze Lyu","submitted_at":"2020-09-09T05:00:02Z","abstract_excerpt":"To alleviate the cold start problem caused by collaborative filtering in recommender systems, knowledge graphs (KGs) are increasingly employed by many methods as auxiliary resources. However, existing work incorporated with KGs cannot capture the explicit long-range semantics between users and items meanwhile consider various connectivity between items. In this paper, we propose RGRec, which combines rule learning and graph neural networks (GNNs) for recommendation. RGRec first maps items to corresponding entities in KGs and adds users as new entities. Then, it automatically learns rules to mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.04104","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/2009.04104/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":"2009.04104","created_at":"2026-07-05T01:34:14.852384+00:00"},{"alias_kind":"arxiv_version","alias_value":"2009.04104v1","created_at":"2026-07-05T01:34:14.852384+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.04104","created_at":"2026-07-05T01:34:14.852384+00:00"},{"alias_kind":"pith_short_12","alias_value":"D3IQY54HXHRT","created_at":"2026-07-05T01:34:14.852384+00:00"},{"alias_kind":"pith_short_16","alias_value":"D3IQY54HXHRTA55C","created_at":"2026-07-05T01:34:14.852384+00:00"},{"alias_kind":"pith_short_8","alias_value":"D3IQY54H","created_at":"2026-07-05T01:34:14.852384+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/D3IQY54HXHRTA55CLZR75Y7N2F","json":"https://pith.science/pith/D3IQY54HXHRTA55CLZR75Y7N2F.json","graph_json":"https://pith.science/api/pith-number/D3IQY54HXHRTA55CLZR75Y7N2F/graph.json","events_json":"https://pith.science/api/pith-number/D3IQY54HXHRTA55CLZR75Y7N2F/events.json","paper":"https://pith.science/paper/D3IQY54H"},"agent_actions":{"view_html":"https://pith.science/pith/D3IQY54HXHRTA55CLZR75Y7N2F","download_json":"https://pith.science/pith/D3IQY54HXHRTA55CLZR75Y7N2F.json","view_paper":"https://pith.science/paper/D3IQY54H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2009.04104&json=true","fetch_graph":"https://pith.science/api/pith-number/D3IQY54HXHRTA55CLZR75Y7N2F/graph.json","fetch_events":"https://pith.science/api/pith-number/D3IQY54HXHRTA55CLZR75Y7N2F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D3IQY54HXHRTA55CLZR75Y7N2F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D3IQY54HXHRTA55CLZR75Y7N2F/action/storage_attestation","attest_author":"https://pith.science/pith/D3IQY54HXHRTA55CLZR75Y7N2F/action/author_attestation","sign_citation":"https://pith.science/pith/D3IQY54HXHRTA55CLZR75Y7N2F/action/citation_signature","submit_replication":"https://pith.science/pith/D3IQY54HXHRTA55CLZR75Y7N2F/action/replication_record"}},"created_at":"2026-07-05T01:34:14.852384+00:00","updated_at":"2026-07-05T01:34:14.852384+00:00"}