{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:HCPYMWW2TO6ODNZQBFC3SUFGD2","short_pith_number":"pith:HCPYMWW2","schema_version":"1.0","canonical_sha256":"389f865ada9bbce1b7300945b950a61e907f885fceab92b9ae3ce5a1fa32dc7c","source":{"kind":"arxiv","id":"2206.02687","version":1},"attestation_state":"computed","paper":{"title":"Multi-Behavior Sequential Recommendation with Temporal Graph Transformer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.IR","authors_text":"Chao Huang, Jian Pei, Lianghao Xia, Yong Xu","submitted_at":"2022-06-06T15:42:54Z","abstract_excerpt":"Modeling time-evolving preferences of users with their sequential item interactions, has attracted increasing attention in many online applications. Hence, sequential recommender systems have been developed to learn the dynamic user interests from the historical interactions for suggesting items. However, the interaction pattern encoding functions in most existing sequential recommender systems have focused on single type of user-item interactions. In many real-life online platforms, user-item interactive behaviors are often multi-typed (e.g., click, add-to-favorite, purchase) with complex cro"},"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":"2206.02687","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2022-06-06T15:42:54Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"4e389c11b686066066c30ef9871c6e5f0c144181c402c4b5b424d456c601491a","abstract_canon_sha256":"652df2674e7d2cf8d37ed998705016d713e892b5ad46dd0c31eebcaa42a534d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:29:20.177708Z","signature_b64":"XcgJ4hk1KgPDpD/ASPYEeGOS0sE0R9n8/XPmnaaMEHKKX/JdxGWI09sZfRyW1Ji/+slEF4iDiV2WVJb0vVFGDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"389f865ada9bbce1b7300945b950a61e907f885fceab92b9ae3ce5a1fa32dc7c","last_reissued_at":"2026-07-05T04:29:20.177241Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:29:20.177241Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Behavior Sequential Recommendation with Temporal Graph Transformer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.IR","authors_text":"Chao Huang, Jian Pei, Lianghao Xia, Yong Xu","submitted_at":"2022-06-06T15:42:54Z","abstract_excerpt":"Modeling time-evolving preferences of users with their sequential item interactions, has attracted increasing attention in many online applications. Hence, sequential recommender systems have been developed to learn the dynamic user interests from the historical interactions for suggesting items. However, the interaction pattern encoding functions in most existing sequential recommender systems have focused on single type of user-item interactions. In many real-life online platforms, user-item interactive behaviors are often multi-typed (e.g., click, add-to-favorite, purchase) with complex cro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.02687","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/2206.02687/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":"2206.02687","created_at":"2026-07-05T04:29:20.177297+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.02687v1","created_at":"2026-07-05T04:29:20.177297+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.02687","created_at":"2026-07-05T04:29:20.177297+00:00"},{"alias_kind":"pith_short_12","alias_value":"HCPYMWW2TO6O","created_at":"2026-07-05T04:29:20.177297+00:00"},{"alias_kind":"pith_short_16","alias_value":"HCPYMWW2TO6ODNZQ","created_at":"2026-07-05T04:29:20.177297+00:00"},{"alias_kind":"pith_short_8","alias_value":"HCPYMWW2","created_at":"2026-07-05T04:29:20.177297+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/HCPYMWW2TO6ODNZQBFC3SUFGD2","json":"https://pith.science/pith/HCPYMWW2TO6ODNZQBFC3SUFGD2.json","graph_json":"https://pith.science/api/pith-number/HCPYMWW2TO6ODNZQBFC3SUFGD2/graph.json","events_json":"https://pith.science/api/pith-number/HCPYMWW2TO6ODNZQBFC3SUFGD2/events.json","paper":"https://pith.science/paper/HCPYMWW2"},"agent_actions":{"view_html":"https://pith.science/pith/HCPYMWW2TO6ODNZQBFC3SUFGD2","download_json":"https://pith.science/pith/HCPYMWW2TO6ODNZQBFC3SUFGD2.json","view_paper":"https://pith.science/paper/HCPYMWW2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.02687&json=true","fetch_graph":"https://pith.science/api/pith-number/HCPYMWW2TO6ODNZQBFC3SUFGD2/graph.json","fetch_events":"https://pith.science/api/pith-number/HCPYMWW2TO6ODNZQBFC3SUFGD2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HCPYMWW2TO6ODNZQBFC3SUFGD2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HCPYMWW2TO6ODNZQBFC3SUFGD2/action/storage_attestation","attest_author":"https://pith.science/pith/HCPYMWW2TO6ODNZQBFC3SUFGD2/action/author_attestation","sign_citation":"https://pith.science/pith/HCPYMWW2TO6ODNZQBFC3SUFGD2/action/citation_signature","submit_replication":"https://pith.science/pith/HCPYMWW2TO6ODNZQBFC3SUFGD2/action/replication_record"}},"created_at":"2026-07-05T04:29:20.177297+00:00","updated_at":"2026-07-05T04:29:20.177297+00:00"}