{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:44HPCFSF2UT2DRC4SUNFTLBUEA","short_pith_number":"pith:44HPCFSF","schema_version":"1.0","canonical_sha256":"e70ef11645d527a1c45c951a59ac34202bdacda105d866b7cdda900b2efa12ba","source":{"kind":"arxiv","id":"2408.07427","version":2},"attestation_state":"computed","paper":{"title":"Beyond Inter-Item Relations: Dynamic Adaption for Enhancing LLM-Based Sequential Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"CanYi Liu, Hui Li, Rongrong Ji, Wei Li, Youchen (Victor) Zhang","submitted_at":"2024-08-14T10:03:40Z","abstract_excerpt":"Sequential recommender systems (SRS) predict the next items that users may prefer based on user historical interaction sequences. Inspired by the rise of large language models (LLMs) in various AI applications, there is a surge of work on LLM-based SRS. Despite their attractive performance, existing LLM-based SRS still exhibit some limitations, including neglecting intra-item relations, ignoring long-term collaborative knowledge and using inflexible architecture designs for adaption. To alleviate these issues, we propose an LLM-based sequential recommendation model named DARec. Built on top of"},"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":"2408.07427","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-08-14T10:03:40Z","cross_cats_sorted":[],"title_canon_sha256":"3e7e8fe6d21a174676ca2a33c3fb8814fcacfc3ef39defc042f9b1f25d4b6053","abstract_canon_sha256":"abc41189dc1cced221ec7a792a99ec4d37d1271b8947782b4728c79f4cced48c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:21:06.973057Z","signature_b64":"27umRzMlxnyl0w2VQZPyqDiRo90Wikm3lirlIMv8/LveMctCsPOdlXzYHU27Gaux8tJD0uFITP71/pPr/n2yAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e70ef11645d527a1c45c951a59ac34202bdacda105d866b7cdda900b2efa12ba","last_reissued_at":"2026-07-05T09:21:06.972580Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:21:06.972580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Inter-Item Relations: Dynamic Adaption for Enhancing LLM-Based Sequential Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"CanYi Liu, Hui Li, Rongrong Ji, Wei Li, Youchen (Victor) Zhang","submitted_at":"2024-08-14T10:03:40Z","abstract_excerpt":"Sequential recommender systems (SRS) predict the next items that users may prefer based on user historical interaction sequences. Inspired by the rise of large language models (LLMs) in various AI applications, there is a surge of work on LLM-based SRS. Despite their attractive performance, existing LLM-based SRS still exhibit some limitations, including neglecting intra-item relations, ignoring long-term collaborative knowledge and using inflexible architecture designs for adaption. To alleviate these issues, we propose an LLM-based sequential recommendation model named DARec. Built on top of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.07427","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/2408.07427/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":"2408.07427","created_at":"2026-07-05T09:21:06.972637+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.07427v2","created_at":"2026-07-05T09:21:06.972637+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.07427","created_at":"2026-07-05T09:21:06.972637+00:00"},{"alias_kind":"pith_short_12","alias_value":"44HPCFSF2UT2","created_at":"2026-07-05T09:21:06.972637+00:00"},{"alias_kind":"pith_short_16","alias_value":"44HPCFSF2UT2DRC4","created_at":"2026-07-05T09:21:06.972637+00:00"},{"alias_kind":"pith_short_8","alias_value":"44HPCFSF","created_at":"2026-07-05T09:21:06.972637+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.18309","citing_title":"LettinGo: Explore User Profile Generation for Recommendation System","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/44HPCFSF2UT2DRC4SUNFTLBUEA","json":"https://pith.science/pith/44HPCFSF2UT2DRC4SUNFTLBUEA.json","graph_json":"https://pith.science/api/pith-number/44HPCFSF2UT2DRC4SUNFTLBUEA/graph.json","events_json":"https://pith.science/api/pith-number/44HPCFSF2UT2DRC4SUNFTLBUEA/events.json","paper":"https://pith.science/paper/44HPCFSF"},"agent_actions":{"view_html":"https://pith.science/pith/44HPCFSF2UT2DRC4SUNFTLBUEA","download_json":"https://pith.science/pith/44HPCFSF2UT2DRC4SUNFTLBUEA.json","view_paper":"https://pith.science/paper/44HPCFSF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.07427&json=true","fetch_graph":"https://pith.science/api/pith-number/44HPCFSF2UT2DRC4SUNFTLBUEA/graph.json","fetch_events":"https://pith.science/api/pith-number/44HPCFSF2UT2DRC4SUNFTLBUEA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/44HPCFSF2UT2DRC4SUNFTLBUEA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/44HPCFSF2UT2DRC4SUNFTLBUEA/action/storage_attestation","attest_author":"https://pith.science/pith/44HPCFSF2UT2DRC4SUNFTLBUEA/action/author_attestation","sign_citation":"https://pith.science/pith/44HPCFSF2UT2DRC4SUNFTLBUEA/action/citation_signature","submit_replication":"https://pith.science/pith/44HPCFSF2UT2DRC4SUNFTLBUEA/action/replication_record"}},"created_at":"2026-07-05T09:21:06.972637+00:00","updated_at":"2026-07-05T09:21:06.972637+00:00"}