{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:YXR5YH7ZI6GRKYOL4IYM42XLXQ","short_pith_number":"pith:YXR5YH7Z","canonical_record":{"source":{"id":"2004.11718","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-04-22T15:13:58Z","cross_cats_sorted":[],"title_canon_sha256":"19ab7f8b2a682c400af74799c74c681da4d82ec87673b71ca38996da162710e4","abstract_canon_sha256":"22910509c9b5c19090a57da1b1dcb96eeb37724bec73a450a5b00663042ff98a"},"schema_version":"1.0"},"canonical_sha256":"c5e3dc1ff9478d1561cbe230ce6aebbc032ba3f62796cab464902dc605162732","source":{"kind":"arxiv","id":"2004.11718","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2004.11718","created_at":"2026-07-05T00:57:57Z"},{"alias_kind":"arxiv_version","alias_value":"2004.11718v1","created_at":"2026-07-05T00:57:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.11718","created_at":"2026-07-05T00:57:57Z"},{"alias_kind":"pith_short_12","alias_value":"YXR5YH7ZI6GR","created_at":"2026-07-05T00:57:57Z"},{"alias_kind":"pith_short_16","alias_value":"YXR5YH7ZI6GRKYOL","created_at":"2026-07-05T00:57:57Z"},{"alias_kind":"pith_short_8","alias_value":"YXR5YH7Z","created_at":"2026-07-05T00:57:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:YXR5YH7ZI6GRKYOL4IYM42XLXQ","target":"record","payload":{"canonical_record":{"source":{"id":"2004.11718","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-04-22T15:13:58Z","cross_cats_sorted":[],"title_canon_sha256":"19ab7f8b2a682c400af74799c74c681da4d82ec87673b71ca38996da162710e4","abstract_canon_sha256":"22910509c9b5c19090a57da1b1dcb96eeb37724bec73a450a5b00663042ff98a"},"schema_version":"1.0"},"canonical_sha256":"c5e3dc1ff9478d1561cbe230ce6aebbc032ba3f62796cab464902dc605162732","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:57:57.042327Z","signature_b64":"6gQ26v0EVuembcL63iWLxnsr494Wr3Rvdg3UEBX1hB9Xypsqyud6zeUCUk7CbWpbAhmz3ZdxXgO0LzWBUkqIDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5e3dc1ff9478d1561cbe230ce6aebbc032ba3f62796cab464902dc605162732","last_reissued_at":"2026-07-05T00:57:57.041787Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:57:57.041787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2004.11718","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:57:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q1+PKK+3JkLBm2QQv7vojdIyAQEVptmTF/Xsm+I0mr16EDr/AB/6Vh70CQMljo4XFPdZFWc9iPDGWe1UXSV3BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T03:11:56.005730Z"},"content_sha256":"93fbcd210bd95d885f1a98793785fc9b0374f807088eee540ff235c3bc4f89ee","schema_version":"1.0","event_id":"sha256:93fbcd210bd95d885f1a98793785fc9b0374f807088eee540ff235c3bc4f89ee"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:YXR5YH7ZI6GRKYOL4IYM42XLXQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph Learning Approaches to Recommender Systems: A Review","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Francesco Ricci, Liang Hu, Longbing Cao, Mehmet Orgun, Nan Wang, Philip S. Yu, Quan Z. Sheng, Shoujin Wang, Xiangnan He, Yan Wang","submitted_at":"2020-04-22T15:13:58Z","abstract_excerpt":"Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ the advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics and popularity for Recommender Systems (RS). Differently from conventional RS, including content based filtering and collaborative filtering, GLRS are built on simple or complex graphs where various objects, e.g., users, items, and attributes, are explicitly or implicitly connected. With the rapid development of graph learning, exploring an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.11718","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/2004.11718/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:57:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BMW/GTpZ/uYAxhkRHtapyQLBpsNRDcNhZnwuOt/1NGcm/uf6qospCxn33kO6EYFugJ0DbXlkJDwM4kcZWUzACA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T03:11:56.006239Z"},"content_sha256":"90da4acde64864edfa6f0502daaff2eb0917c212423a1fb48187496afbeea2a0","schema_version":"1.0","event_id":"sha256:90da4acde64864edfa6f0502daaff2eb0917c212423a1fb48187496afbeea2a0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YXR5YH7ZI6GRKYOL4IYM42XLXQ/bundle.json","state_url":"https://pith.science/pith/YXR5YH7ZI6GRKYOL4IYM42XLXQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YXR5YH7ZI6GRKYOL4IYM42XLXQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-16T03:11:56Z","links":{"resolver":"https://pith.science/pith/YXR5YH7ZI6GRKYOL4IYM42XLXQ","bundle":"https://pith.science/pith/YXR5YH7ZI6GRKYOL4IYM42XLXQ/bundle.json","state":"https://pith.science/pith/YXR5YH7ZI6GRKYOL4IYM42XLXQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YXR5YH7ZI6GRKYOL4IYM42XLXQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:YXR5YH7ZI6GRKYOL4IYM42XLXQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"22910509c9b5c19090a57da1b1dcb96eeb37724bec73a450a5b00663042ff98a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-04-22T15:13:58Z","title_canon_sha256":"19ab7f8b2a682c400af74799c74c681da4d82ec87673b71ca38996da162710e4"},"schema_version":"1.0","source":{"id":"2004.11718","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2004.11718","created_at":"2026-07-05T00:57:57Z"},{"alias_kind":"arxiv_version","alias_value":"2004.11718v1","created_at":"2026-07-05T00:57:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.11718","created_at":"2026-07-05T00:57:57Z"},{"alias_kind":"pith_short_12","alias_value":"YXR5YH7ZI6GR","created_at":"2026-07-05T00:57:57Z"},{"alias_kind":"pith_short_16","alias_value":"YXR5YH7ZI6GRKYOL","created_at":"2026-07-05T00:57:57Z"},{"alias_kind":"pith_short_8","alias_value":"YXR5YH7Z","created_at":"2026-07-05T00:57:57Z"}],"graph_snapshots":[{"event_id":"sha256:90da4acde64864edfa6f0502daaff2eb0917c212423a1fb48187496afbeea2a0","target":"graph","created_at":"2026-07-05T00:57:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2004.11718/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ the advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics and popularity for Recommender Systems (RS). Differently from conventional RS, including content based filtering and collaborative filtering, GLRS are built on simple or complex graphs where various objects, e.g., users, items, and attributes, are explicitly or implicitly connected. With the rapid development of graph learning, exploring an","authors_text":"Francesco Ricci, Liang Hu, Longbing Cao, Mehmet Orgun, Nan Wang, Philip S. Yu, Quan Z. Sheng, Shoujin Wang, Xiangnan He, Yan Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-04-22T15:13:58Z","title":"Graph Learning Approaches to Recommender Systems: A Review"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.11718","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:93fbcd210bd95d885f1a98793785fc9b0374f807088eee540ff235c3bc4f89ee","target":"record","created_at":"2026-07-05T00:57:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"22910509c9b5c19090a57da1b1dcb96eeb37724bec73a450a5b00663042ff98a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-04-22T15:13:58Z","title_canon_sha256":"19ab7f8b2a682c400af74799c74c681da4d82ec87673b71ca38996da162710e4"},"schema_version":"1.0","source":{"id":"2004.11718","kind":"arxiv","version":1}},"canonical_sha256":"c5e3dc1ff9478d1561cbe230ce6aebbc032ba3f62796cab464902dc605162732","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c5e3dc1ff9478d1561cbe230ce6aebbc032ba3f62796cab464902dc605162732","first_computed_at":"2026-07-05T00:57:57.041787Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:57:57.041787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6gQ26v0EVuembcL63iWLxnsr494Wr3Rvdg3UEBX1hB9Xypsqyud6zeUCUk7CbWpbAhmz3ZdxXgO0LzWBUkqIDg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:57:57.042327Z","signed_message":"canonical_sha256_bytes"},"source_id":"2004.11718","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:93fbcd210bd95d885f1a98793785fc9b0374f807088eee540ff235c3bc4f89ee","sha256:90da4acde64864edfa6f0502daaff2eb0917c212423a1fb48187496afbeea2a0"],"state_sha256":"ca246c792327103136cc52f5ce8a675d545351437aa019dc0df73950afeadc6b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WfoCTYzyp5vZwV4FHhL6IW4EcE4LZ0yIislVdO9jRTEq597jUb5xXHo3LLzr+xSF0gMbhtCo8X/douxF6DDCDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T03:11:56.010085Z","bundle_sha256":"40e8da49f185cb37771af3754c76d0a787c9f75ab2a0180ec436e0cbb46ab8bc"}}