{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:5ZQIPCSRHY4QK7FSK5WDZ5TRCL","short_pith_number":"pith:5ZQIPCSR","canonical_record":{"source":{"id":"2101.01433","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SI","submitted_at":"2021-01-05T09:54:25Z","cross_cats_sorted":[],"title_canon_sha256":"bd973177e6df1b2ef96e9d950d663867f6a1848e218257e7ff19788d61b47db2","abstract_canon_sha256":"d369f7632cae39f19f62d47124c274d54ba86d6dccadf90f83dfad1c64bba4ff"},"schema_version":"1.0"},"canonical_sha256":"ee60878a513e39057cb2576c3cf67112de705e4c32f7cff0ff3bfbdb692fcb69","source":{"kind":"arxiv","id":"2101.01433","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.01433","created_at":"2026-07-05T02:04:33Z"},{"alias_kind":"arxiv_version","alias_value":"2101.01433v1","created_at":"2026-07-05T02:04:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.01433","created_at":"2026-07-05T02:04:33Z"},{"alias_kind":"pith_short_12","alias_value":"5ZQIPCSRHY4Q","created_at":"2026-07-05T02:04:33Z"},{"alias_kind":"pith_short_16","alias_value":"5ZQIPCSRHY4QK7FS","created_at":"2026-07-05T02:04:33Z"},{"alias_kind":"pith_short_8","alias_value":"5ZQIPCSR","created_at":"2026-07-05T02:04:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:5ZQIPCSRHY4QK7FSK5WDZ5TRCL","target":"record","payload":{"canonical_record":{"source":{"id":"2101.01433","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SI","submitted_at":"2021-01-05T09:54:25Z","cross_cats_sorted":[],"title_canon_sha256":"bd973177e6df1b2ef96e9d950d663867f6a1848e218257e7ff19788d61b47db2","abstract_canon_sha256":"d369f7632cae39f19f62d47124c274d54ba86d6dccadf90f83dfad1c64bba4ff"},"schema_version":"1.0"},"canonical_sha256":"ee60878a513e39057cb2576c3cf67112de705e4c32f7cff0ff3bfbdb692fcb69","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:04:33.124003Z","signature_b64":"FaBJxKFulNLUclJxURidNXTTWktwOLQYACEEVlq6gzxzaZNO5UQOjKnuDj8omkfBwhO/vUOHt7lTtC4Fr9rqBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee60878a513e39057cb2576c3cf67112de705e4c32f7cff0ff3bfbdb692fcb69","last_reissued_at":"2026-07-05T02:04:33.123644Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:04:33.123644Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2101.01433","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":"2026-07-05T02:04:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l652gR52jO6AVdgL0DWEjUzZo9eGmAtGlKiMwxJuH18JJ1rBnAf90netuC0kQi2ey8zpEEMRKWdVgJihIfifAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-19T23:27:03.881535Z"},"content_sha256":"26cac62a6919b86788dc25d0dfa2ce95980c95fc289243824f633891fe0c1630","schema_version":"1.0","event_id":"sha256:26cac62a6919b86788dc25d0dfa2ce95980c95fc289243824f633891fe0c1630"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:5ZQIPCSRHY4QK7FSK5WDZ5TRCL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Temporal Meta-path Guided Explainable Recommendation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Guandong Xu, Hongxu Chen, Hongzhi Yin, Xiangguo Sun, Yicong Li","submitted_at":"2021-01-05T09:54:25Z","abstract_excerpt":"This paper utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations. Compared with existing works that use heavy recurrent neural networks to model temporal information, we propose simple but effective neural networks to capture user historical item features and path-based context to characterise next purchased item. Extensive evaluations of TMER on three real-world benchmark datasets show state-of-the-art performance compared against recent s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.01433","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/2101.01433/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":"2026-07-05T02:04:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Aqu+EalS9ZwHEK+BVBGL2lFgKy/7wPCPYFqX0QeZxWpt2umJzI40ZPL6pPePWJ9k34a1qBqAIaYTO6NK6M4mAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-19T23:27:03.881908Z"},"content_sha256":"f041850efa08af822d0e04de629a94e2e24dc86b8e61eb640265c80d892fa2e4","schema_version":"1.0","event_id":"sha256:f041850efa08af822d0e04de629a94e2e24dc86b8e61eb640265c80d892fa2e4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5ZQIPCSRHY4QK7FSK5WDZ5TRCL/bundle.json","state_url":"https://pith.science/pith/5ZQIPCSRHY4QK7FSK5WDZ5TRCL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5ZQIPCSRHY4QK7FSK5WDZ5TRCL/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":"2026-07-19T23:27:03Z","links":{"resolver":"https://pith.science/pith/5ZQIPCSRHY4QK7FSK5WDZ5TRCL","bundle":"https://pith.science/pith/5ZQIPCSRHY4QK7FSK5WDZ5TRCL/bundle.json","state":"https://pith.science/pith/5ZQIPCSRHY4QK7FSK5WDZ5TRCL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5ZQIPCSRHY4QK7FSK5WDZ5TRCL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:5ZQIPCSRHY4QK7FSK5WDZ5TRCL","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":"d369f7632cae39f19f62d47124c274d54ba86d6dccadf90f83dfad1c64bba4ff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SI","submitted_at":"2021-01-05T09:54:25Z","title_canon_sha256":"bd973177e6df1b2ef96e9d950d663867f6a1848e218257e7ff19788d61b47db2"},"schema_version":"1.0","source":{"id":"2101.01433","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.01433","created_at":"2026-07-05T02:04:33Z"},{"alias_kind":"arxiv_version","alias_value":"2101.01433v1","created_at":"2026-07-05T02:04:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.01433","created_at":"2026-07-05T02:04:33Z"},{"alias_kind":"pith_short_12","alias_value":"5ZQIPCSRHY4Q","created_at":"2026-07-05T02:04:33Z"},{"alias_kind":"pith_short_16","alias_value":"5ZQIPCSRHY4QK7FS","created_at":"2026-07-05T02:04:33Z"},{"alias_kind":"pith_short_8","alias_value":"5ZQIPCSR","created_at":"2026-07-05T02:04:33Z"}],"graph_snapshots":[{"event_id":"sha256:f041850efa08af822d0e04de629a94e2e24dc86b8e61eb640265c80d892fa2e4","target":"graph","created_at":"2026-07-05T02:04:33Z","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/2101.01433/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations. Compared with existing works that use heavy recurrent neural networks to model temporal information, we propose simple but effective neural networks to capture user historical item features and path-based context to characterise next purchased item. Extensive evaluations of TMER on three real-world benchmark datasets show state-of-the-art performance compared against recent s","authors_text":"Guandong Xu, Hongxu Chen, Hongzhi Yin, Xiangguo Sun, Yicong Li","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SI","submitted_at":"2021-01-05T09:54:25Z","title":"Temporal Meta-path Guided Explainable Recommendation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.01433","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":"sha256:26cac62a6919b86788dc25d0dfa2ce95980c95fc289243824f633891fe0c1630","target":"record","created_at":"2026-07-05T02:04:33Z","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":"d369f7632cae39f19f62d47124c274d54ba86d6dccadf90f83dfad1c64bba4ff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SI","submitted_at":"2021-01-05T09:54:25Z","title_canon_sha256":"bd973177e6df1b2ef96e9d950d663867f6a1848e218257e7ff19788d61b47db2"},"schema_version":"1.0","source":{"id":"2101.01433","kind":"arxiv","version":1}},"canonical_sha256":"ee60878a513e39057cb2576c3cf67112de705e4c32f7cff0ff3bfbdb692fcb69","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ee60878a513e39057cb2576c3cf67112de705e4c32f7cff0ff3bfbdb692fcb69","first_computed_at":"2026-07-05T02:04:33.123644Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:04:33.123644Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FaBJxKFulNLUclJxURidNXTTWktwOLQYACEEVlq6gzxzaZNO5UQOjKnuDj8omkfBwhO/vUOHt7lTtC4Fr9rqBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:04:33.124003Z","signed_message":"canonical_sha256_bytes"},"source_id":"2101.01433","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:26cac62a6919b86788dc25d0dfa2ce95980c95fc289243824f633891fe0c1630","sha256:f041850efa08af822d0e04de629a94e2e24dc86b8e61eb640265c80d892fa2e4"],"state_sha256":"59a2b2b42581597ce1e51fefad2e84a92c912c19861669047e4923baca65f658"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"REl4qt6YI9wdRlw4eUOtxOGTY555LB08nr1pIFRUCF7oI5moptZfHBGMZvQ9N8zOTcT5xKfRA7smMxEp7yu3Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-19T23:27:03.884488Z","bundle_sha256":"d6a335f4c1d2a854f29baceebdf968edbd9e5567a4a19c3b02ebf8fc17fae49d"}}