{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UKQC6OA45LHMZRF3BOBZL2AR74","short_pith_number":"pith:UKQC6OA4","schema_version":"1.0","canonical_sha256":"a2a02f381ceaceccc4bb0b8395e811ff291356a7edfa5d0e6ec0f04d5fb88059","source":{"kind":"arxiv","id":"2406.03085","version":1},"attestation_state":"computed","paper":{"title":"Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.LG","authors_text":"Defu Lian, Enhong Chen, Hao Wang, Jiaqing Zhang, Liangyue Li, Sirui Zhao, Tingjia Shen, Zulong Chen","submitted_at":"2024-06-05T09:19:54Z","abstract_excerpt":"Cross-Domain Sequential Recommendation (CDSR) aims to mine and transfer users' sequential preferences across different domains to alleviate the long-standing cold-start issue. Traditional CDSR models capture collaborative information through user and item modeling while overlooking valuable semantic information. Recently, Large Language Model (LLM) has demonstrated powerful semantic reasoning capabilities, motivating us to introduce them to better capture semantic information. However, introducing LLMs to CDSR is non-trivial due to two crucial issues: seamless information integration and domai"},"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":"2406.03085","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-05T09:19:54Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"6ae83c5ff8c55845fb9287a9061e9b6535cafa7a5e706f5e8707173b179e6fcf","abstract_canon_sha256":"7a25f3d9ba1202bd09ceb37b5d4b32692e029896a842bc2f313d7c2690a05834"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:27:51.151013Z","signature_b64":"G0d9jhNM1INfm07jV9bukkRdCC6o6R1rSwJAB5wASZ7dCZONEC1htx0h4lDbCJjxJItrSTFpqvzHMXa+jXDyAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a2a02f381ceaceccc4bb0b8395e811ff291356a7edfa5d0e6ec0f04d5fb88059","last_reissued_at":"2026-07-05T08:27:51.150565Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:27:51.150565Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.LG","authors_text":"Defu Lian, Enhong Chen, Hao Wang, Jiaqing Zhang, Liangyue Li, Sirui Zhao, Tingjia Shen, Zulong Chen","submitted_at":"2024-06-05T09:19:54Z","abstract_excerpt":"Cross-Domain Sequential Recommendation (CDSR) aims to mine and transfer users' sequential preferences across different domains to alleviate the long-standing cold-start issue. Traditional CDSR models capture collaborative information through user and item modeling while overlooking valuable semantic information. Recently, Large Language Model (LLM) has demonstrated powerful semantic reasoning capabilities, motivating us to introduce them to better capture semantic information. However, introducing LLMs to CDSR is non-trivial due to two crucial issues: seamless information integration and domai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.03085","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/2406.03085/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":"2406.03085","created_at":"2026-07-05T08:27:51.150626+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.03085v1","created_at":"2026-07-05T08:27:51.150626+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.03085","created_at":"2026-07-05T08:27:51.150626+00:00"},{"alias_kind":"pith_short_12","alias_value":"UKQC6OA45LHM","created_at":"2026-07-05T08:27:51.150626+00:00"},{"alias_kind":"pith_short_16","alias_value":"UKQC6OA45LHMZRF3","created_at":"2026-07-05T08:27:51.150626+00:00"},{"alias_kind":"pith_short_8","alias_value":"UKQC6OA4","created_at":"2026-07-05T08:27:51.150626+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.14930","citing_title":"IE as Cache: Information Extraction Enhanced Agentic Reasoning","ref_index":55,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15621","citing_title":"Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02369","citing_title":"Bridging Behavior and Semantics for Time-aware Cross-Domain Sequential Recommendation","ref_index":43,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UKQC6OA45LHMZRF3BOBZL2AR74","json":"https://pith.science/pith/UKQC6OA45LHMZRF3BOBZL2AR74.json","graph_json":"https://pith.science/api/pith-number/UKQC6OA45LHMZRF3BOBZL2AR74/graph.json","events_json":"https://pith.science/api/pith-number/UKQC6OA45LHMZRF3BOBZL2AR74/events.json","paper":"https://pith.science/paper/UKQC6OA4"},"agent_actions":{"view_html":"https://pith.science/pith/UKQC6OA45LHMZRF3BOBZL2AR74","download_json":"https://pith.science/pith/UKQC6OA45LHMZRF3BOBZL2AR74.json","view_paper":"https://pith.science/paper/UKQC6OA4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.03085&json=true","fetch_graph":"https://pith.science/api/pith-number/UKQC6OA45LHMZRF3BOBZL2AR74/graph.json","fetch_events":"https://pith.science/api/pith-number/UKQC6OA45LHMZRF3BOBZL2AR74/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UKQC6OA45LHMZRF3BOBZL2AR74/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UKQC6OA45LHMZRF3BOBZL2AR74/action/storage_attestation","attest_author":"https://pith.science/pith/UKQC6OA45LHMZRF3BOBZL2AR74/action/author_attestation","sign_citation":"https://pith.science/pith/UKQC6OA45LHMZRF3BOBZL2AR74/action/citation_signature","submit_replication":"https://pith.science/pith/UKQC6OA45LHMZRF3BOBZL2AR74/action/replication_record"}},"created_at":"2026-07-05T08:27:51.150626+00:00","updated_at":"2026-07-05T08:27:51.150626+00:00"}