{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:K6PTYXZ7QP7VVGJFTT5XS6CZCK","short_pith_number":"pith:K6PTYXZ7","schema_version":"1.0","canonical_sha256":"579f3c5f3f83ff5a99259cfb7978591292f2b030438636b6b63b78d889fbb14a","source":{"kind":"arxiv","id":"2205.01286","version":1},"attestation_state":"computed","paper":{"title":"When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Chenliang Li, Jianxin Chang, Yang Song, Yannan Niu, Yu Tian","submitted_at":"2022-05-03T03:21:29Z","abstract_excerpt":"Sequential recommendation aims at identifying the next item that is preferred by a user based on their behavioral history. Compared to conventional sequential models that leverage attention mechanisms and RNNs, recent efforts mainly follow two directions for improvement: multi-interest learning and graph convolutional aggregation. Specifically, multi-interest methods such as ComiRec and MIMN, focus on extracting different interests for a user by performing historical item clustering, while graph convolution methods including TGSRec and SURGE elect to refine user preferences based on multi-leve"},"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":"2205.01286","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2022-05-03T03:21:29Z","cross_cats_sorted":[],"title_canon_sha256":"62bef9d58353337499758158cb0538822599671415b57d06b82e7487ae54b19b","abstract_canon_sha256":"dbc05802edd96f44ee673de6427501beeba64da43adfac0f5326907a793660bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:20:03.076160Z","signature_b64":"vDY5e5xAGzcjDFC8rdNBECkIc6Ma1lu5oV8R/+4Xc5iNRq+wyf88FqkEVwbiS4OXmPuwRiHMdQwS4EDW6vXTAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"579f3c5f3f83ff5a99259cfb7978591292f2b030438636b6b63b78d889fbb14a","last_reissued_at":"2026-07-05T04:20:03.075780Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:20:03.075780Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Chenliang Li, Jianxin Chang, Yang Song, Yannan Niu, Yu Tian","submitted_at":"2022-05-03T03:21:29Z","abstract_excerpt":"Sequential recommendation aims at identifying the next item that is preferred by a user based on their behavioral history. Compared to conventional sequential models that leverage attention mechanisms and RNNs, recent efforts mainly follow two directions for improvement: multi-interest learning and graph convolutional aggregation. Specifically, multi-interest methods such as ComiRec and MIMN, focus on extracting different interests for a user by performing historical item clustering, while graph convolution methods including TGSRec and SURGE elect to refine user preferences based on multi-leve"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.01286","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/2205.01286/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":"2205.01286","created_at":"2026-07-05T04:20:03.075841+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.01286v1","created_at":"2026-07-05T04:20:03.075841+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.01286","created_at":"2026-07-05T04:20:03.075841+00:00"},{"alias_kind":"pith_short_12","alias_value":"K6PTYXZ7QP7V","created_at":"2026-07-05T04:20:03.075841+00:00"},{"alias_kind":"pith_short_16","alias_value":"K6PTYXZ7QP7VVGJF","created_at":"2026-07-05T04:20:03.075841+00:00"},{"alias_kind":"pith_short_8","alias_value":"K6PTYXZ7","created_at":"2026-07-05T04:20:03.075841+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/K6PTYXZ7QP7VVGJFTT5XS6CZCK","json":"https://pith.science/pith/K6PTYXZ7QP7VVGJFTT5XS6CZCK.json","graph_json":"https://pith.science/api/pith-number/K6PTYXZ7QP7VVGJFTT5XS6CZCK/graph.json","events_json":"https://pith.science/api/pith-number/K6PTYXZ7QP7VVGJFTT5XS6CZCK/events.json","paper":"https://pith.science/paper/K6PTYXZ7"},"agent_actions":{"view_html":"https://pith.science/pith/K6PTYXZ7QP7VVGJFTT5XS6CZCK","download_json":"https://pith.science/pith/K6PTYXZ7QP7VVGJFTT5XS6CZCK.json","view_paper":"https://pith.science/paper/K6PTYXZ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.01286&json=true","fetch_graph":"https://pith.science/api/pith-number/K6PTYXZ7QP7VVGJFTT5XS6CZCK/graph.json","fetch_events":"https://pith.science/api/pith-number/K6PTYXZ7QP7VVGJFTT5XS6CZCK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K6PTYXZ7QP7VVGJFTT5XS6CZCK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K6PTYXZ7QP7VVGJFTT5XS6CZCK/action/storage_attestation","attest_author":"https://pith.science/pith/K6PTYXZ7QP7VVGJFTT5XS6CZCK/action/author_attestation","sign_citation":"https://pith.science/pith/K6PTYXZ7QP7VVGJFTT5XS6CZCK/action/citation_signature","submit_replication":"https://pith.science/pith/K6PTYXZ7QP7VVGJFTT5XS6CZCK/action/replication_record"}},"created_at":"2026-07-05T04:20:03.075841+00:00","updated_at":"2026-07-05T04:20:03.075841+00:00"}