{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JLH3T44X2ML35VS7QAAYRWZK25","short_pith_number":"pith:JLH3T44X","schema_version":"1.0","canonical_sha256":"4acfb9f397d317bed65f800188db2ad767be52634c05c5b0eb082d5dd63327e5","source":{"kind":"arxiv","id":"2502.10157","version":2},"attestation_state":"computed","paper":{"title":"SessionRec: Next Session Prediction Paradigm For Generative Sequential Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Daoyuan Wang, Hao Guo, Jinpeng Wang, Lei Huang, Lei Wang, Linzhi Peng, Long Zhang, Sheng Chen, Shichao Wang, Xiaoteng Wang","submitted_at":"2025-02-14T13:36:20Z","abstract_excerpt":"We introduce SessionRec, a novel next-session prediction paradigm (NSPP) for generative sequential recommendation, addressing the fundamental misalignment between conventional next-item prediction paradigm (NIPP) and real-world recommendation scenarios. Unlike NIPP's item-level autoregressive generation that contradicts actual session-based user interactions, our framework introduces a session-aware representation learning through hierarchical sequence aggregation (intra/inter-session), reducing attention computation complexity while enabling implicit modeling of massive negative interactions,"},"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":"2502.10157","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-02-14T13:36:20Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8319d1616db9f1ac3a4ef23c03e901144da04e811f688b5f2d80de4dd5ed62d9","abstract_canon_sha256":"8cac8bd4227f20300dd1a4b0bd95d4bed5b836a3ae9e4ea7a8463719ea4a21bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:15:49.239903Z","signature_b64":"Rh2PeFcgPKKQI5aq8dO8mdalN2owkwEJ8+LZnGKst+IdWO0PK/NUOprxHFKnDCtMo00arIR9HpsYHAl5hNFSCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4acfb9f397d317bed65f800188db2ad767be52634c05c5b0eb082d5dd63327e5","last_reissued_at":"2026-07-05T10:15:49.239361Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:15:49.239361Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SessionRec: Next Session Prediction Paradigm For Generative Sequential Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Daoyuan Wang, Hao Guo, Jinpeng Wang, Lei Huang, Lei Wang, Linzhi Peng, Long Zhang, Sheng Chen, Shichao Wang, Xiaoteng Wang","submitted_at":"2025-02-14T13:36:20Z","abstract_excerpt":"We introduce SessionRec, a novel next-session prediction paradigm (NSPP) for generative sequential recommendation, addressing the fundamental misalignment between conventional next-item prediction paradigm (NIPP) and real-world recommendation scenarios. Unlike NIPP's item-level autoregressive generation that contradicts actual session-based user interactions, our framework introduces a session-aware representation learning through hierarchical sequence aggregation (intra/inter-session), reducing attention computation complexity while enabling implicit modeling of massive negative interactions,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.10157","kind":"arxiv","version":2},"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/2502.10157/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":"2502.10157","created_at":"2026-07-05T10:15:49.239442+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.10157v2","created_at":"2026-07-05T10:15:49.239442+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.10157","created_at":"2026-07-05T10:15:49.239442+00:00"},{"alias_kind":"pith_short_12","alias_value":"JLH3T44X2ML3","created_at":"2026-07-05T10:15:49.239442+00:00"},{"alias_kind":"pith_short_16","alias_value":"JLH3T44X2ML35VS7","created_at":"2026-07-05T10:15:49.239442+00:00"},{"alias_kind":"pith_short_8","alias_value":"JLH3T44X","created_at":"2026-07-05T10:15:49.239442+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.12704","citing_title":"PinFM: Foundation Model for User Activity Sequences at a Billion-scale Visual Discovery Platform","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JLH3T44X2ML35VS7QAAYRWZK25","json":"https://pith.science/pith/JLH3T44X2ML35VS7QAAYRWZK25.json","graph_json":"https://pith.science/api/pith-number/JLH3T44X2ML35VS7QAAYRWZK25/graph.json","events_json":"https://pith.science/api/pith-number/JLH3T44X2ML35VS7QAAYRWZK25/events.json","paper":"https://pith.science/paper/JLH3T44X"},"agent_actions":{"view_html":"https://pith.science/pith/JLH3T44X2ML35VS7QAAYRWZK25","download_json":"https://pith.science/pith/JLH3T44X2ML35VS7QAAYRWZK25.json","view_paper":"https://pith.science/paper/JLH3T44X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.10157&json=true","fetch_graph":"https://pith.science/api/pith-number/JLH3T44X2ML35VS7QAAYRWZK25/graph.json","fetch_events":"https://pith.science/api/pith-number/JLH3T44X2ML35VS7QAAYRWZK25/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JLH3T44X2ML35VS7QAAYRWZK25/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JLH3T44X2ML35VS7QAAYRWZK25/action/storage_attestation","attest_author":"https://pith.science/pith/JLH3T44X2ML35VS7QAAYRWZK25/action/author_attestation","sign_citation":"https://pith.science/pith/JLH3T44X2ML35VS7QAAYRWZK25/action/citation_signature","submit_replication":"https://pith.science/pith/JLH3T44X2ML35VS7QAAYRWZK25/action/replication_record"}},"created_at":"2026-07-05T10:15:49.239442+00:00","updated_at":"2026-07-05T10:15:49.239442+00:00"}