{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:FIRVFC5LB5RO52VIUTPHRMGONS","short_pith_number":"pith:FIRVFC5L","schema_version":"1.0","canonical_sha256":"2a23528bab0f62eeeaa8a4de78b0ce6c8016eb65ddb0793b8f5a48dc49f551d0","source":{"kind":"arxiv","id":"2109.11790","version":1},"attestation_state":"computed","paper":{"title":"Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Gang Wang, Jianyong Wang, Junchi Yan, Wei Zhang, Zeyuan Chen","submitted_at":"2021-09-24T07:44:27Z","abstract_excerpt":"Sequential Recommendation aims to recommend items that a target user will interact with in the near future based on the historically interacted items. While modeling temporal dynamics is crucial for sequential recommendation, most of the existing studies concentrate solely on the user side while overlooking the sequential patterns existing in the counterpart, i.e., the item side. Although a few studies investigate the dynamics involved in the dual sides, the complex user-item interactions are not fully exploited from a global perspective to derive dynamic user and item representations. In this"},"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":"2109.11790","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2021-09-24T07:44:27Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9c9269c39e3e654ba79f183c055f69d8bf5ec1f896505e2a30d4eab3ca6f2dec","abstract_canon_sha256":"7112b6f6ffb841b3cf735d7b04e2f1eb45ab980bf93ec07349c887b4d4ae8e63"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:17:07.490287Z","signature_b64":"GwBji5SkPbM6mS1hKAwqp7riUY3Bq7Tcsjb/bu8Qdy8jlzN9/owE7ZW/Z0odsSAH6/0Ww4/daIbpTgcI05roBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2a23528bab0f62eeeaa8a4de78b0ce6c8016eb65ddb0793b8f5a48dc49f551d0","last_reissued_at":"2026-07-05T03:17:07.489514Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:17:07.489514Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Gang Wang, Jianyong Wang, Junchi Yan, Wei Zhang, Zeyuan Chen","submitted_at":"2021-09-24T07:44:27Z","abstract_excerpt":"Sequential Recommendation aims to recommend items that a target user will interact with in the near future based on the historically interacted items. While modeling temporal dynamics is crucial for sequential recommendation, most of the existing studies concentrate solely on the user side while overlooking the sequential patterns existing in the counterpart, i.e., the item side. Although a few studies investigate the dynamics involved in the dual sides, the complex user-item interactions are not fully exploited from a global perspective to derive dynamic user and item representations. In this"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.11790","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/2109.11790/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":"2109.11790","created_at":"2026-07-05T03:17:07.489572+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.11790v1","created_at":"2026-07-05T03:17:07.489572+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.11790","created_at":"2026-07-05T03:17:07.489572+00:00"},{"alias_kind":"pith_short_12","alias_value":"FIRVFC5LB5RO","created_at":"2026-07-05T03:17:07.489572+00:00"},{"alias_kind":"pith_short_16","alias_value":"FIRVFC5LB5RO52VI","created_at":"2026-07-05T03:17:07.489572+00:00"},{"alias_kind":"pith_short_8","alias_value":"FIRVFC5L","created_at":"2026-07-05T03:17:07.489572+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/FIRVFC5LB5RO52VIUTPHRMGONS","json":"https://pith.science/pith/FIRVFC5LB5RO52VIUTPHRMGONS.json","graph_json":"https://pith.science/api/pith-number/FIRVFC5LB5RO52VIUTPHRMGONS/graph.json","events_json":"https://pith.science/api/pith-number/FIRVFC5LB5RO52VIUTPHRMGONS/events.json","paper":"https://pith.science/paper/FIRVFC5L"},"agent_actions":{"view_html":"https://pith.science/pith/FIRVFC5LB5RO52VIUTPHRMGONS","download_json":"https://pith.science/pith/FIRVFC5LB5RO52VIUTPHRMGONS.json","view_paper":"https://pith.science/paper/FIRVFC5L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.11790&json=true","fetch_graph":"https://pith.science/api/pith-number/FIRVFC5LB5RO52VIUTPHRMGONS/graph.json","fetch_events":"https://pith.science/api/pith-number/FIRVFC5LB5RO52VIUTPHRMGONS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FIRVFC5LB5RO52VIUTPHRMGONS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FIRVFC5LB5RO52VIUTPHRMGONS/action/storage_attestation","attest_author":"https://pith.science/pith/FIRVFC5LB5RO52VIUTPHRMGONS/action/author_attestation","sign_citation":"https://pith.science/pith/FIRVFC5LB5RO52VIUTPHRMGONS/action/citation_signature","submit_replication":"https://pith.science/pith/FIRVFC5LB5RO52VIUTPHRMGONS/action/replication_record"}},"created_at":"2026-07-05T03:17:07.489572+00:00","updated_at":"2026-07-05T03:17:07.489572+00:00"}