{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JDHXKWUA3VRFLSGR3IA7ZQ6LWY","short_pith_number":"pith:JDHXKWUA","schema_version":"1.0","canonical_sha256":"48cf755a80dd6255c8d1da01fcc3cbb62570da92a39542e2a640d1664e717274","source":{"kind":"arxiv","id":"2403.10135","version":1},"attestation_state":"computed","paper":{"title":"The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.IR","authors_text":"Ee-Peng Lim, Lei Wang","submitted_at":"2024-03-15T09:28:19Z","abstract_excerpt":"Large language models (LLMs) have shown excellent performance on various NLP tasks. To use LLMs as strong sequential recommenders, we explore the in-context learning approach to sequential recommendation. We investigate the effects of instruction format, task consistency, demonstration selection, and number of demonstrations. As increasing the number of demonstrations in ICL does not improve accuracy despite using a long prompt, we propose a novel method called LLMSRec-Syn that incorporates multiple demonstration users into one aggregated demonstration. Our experiments on three recommendation "},"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":"2403.10135","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-03-15T09:28:19Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"86309dc156f2ab3939f7543ef3ca78a4dff3027081e8f8d8122334b09a80ba4d","abstract_canon_sha256":"fc0befb2763f820c67ae8bc28afbf1880488bc692f7894dd8475c8f65448f3e6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:56:30.760165Z","signature_b64":"ydj6wDYK072UHnBqQrpf7pV4auzk1HMygAoWfrmty9ImXuZC2Sy+nKgjxIvbvj80IHZ2urVF5mWT3EFu2PSzCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48cf755a80dd6255c8d1da01fcc3cbb62570da92a39542e2a640d1664e717274","last_reissued_at":"2026-07-05T07:56:30.759685Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:56:30.759685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.IR","authors_text":"Ee-Peng Lim, Lei Wang","submitted_at":"2024-03-15T09:28:19Z","abstract_excerpt":"Large language models (LLMs) have shown excellent performance on various NLP tasks. To use LLMs as strong sequential recommenders, we explore the in-context learning approach to sequential recommendation. We investigate the effects of instruction format, task consistency, demonstration selection, and number of demonstrations. As increasing the number of demonstrations in ICL does not improve accuracy despite using a long prompt, we propose a novel method called LLMSRec-Syn that incorporates multiple demonstration users into one aggregated demonstration. Our experiments on three recommendation "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.10135","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/2403.10135/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":"2403.10135","created_at":"2026-07-05T07:56:30.759743+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.10135v1","created_at":"2026-07-05T07:56:30.759743+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.10135","created_at":"2026-07-05T07:56:30.759743+00:00"},{"alias_kind":"pith_short_12","alias_value":"JDHXKWUA3VRF","created_at":"2026-07-05T07:56:30.759743+00:00"},{"alias_kind":"pith_short_16","alias_value":"JDHXKWUA3VRFLSGR","created_at":"2026-07-05T07:56:30.759743+00:00"},{"alias_kind":"pith_short_8","alias_value":"JDHXKWUA","created_at":"2026-07-05T07:56:30.759743+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.02713","citing_title":"Open-Set Living Need Prediction with Large Language Models","ref_index":53,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JDHXKWUA3VRFLSGR3IA7ZQ6LWY","json":"https://pith.science/pith/JDHXKWUA3VRFLSGR3IA7ZQ6LWY.json","graph_json":"https://pith.science/api/pith-number/JDHXKWUA3VRFLSGR3IA7ZQ6LWY/graph.json","events_json":"https://pith.science/api/pith-number/JDHXKWUA3VRFLSGR3IA7ZQ6LWY/events.json","paper":"https://pith.science/paper/JDHXKWUA"},"agent_actions":{"view_html":"https://pith.science/pith/JDHXKWUA3VRFLSGR3IA7ZQ6LWY","download_json":"https://pith.science/pith/JDHXKWUA3VRFLSGR3IA7ZQ6LWY.json","view_paper":"https://pith.science/paper/JDHXKWUA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.10135&json=true","fetch_graph":"https://pith.science/api/pith-number/JDHXKWUA3VRFLSGR3IA7ZQ6LWY/graph.json","fetch_events":"https://pith.science/api/pith-number/JDHXKWUA3VRFLSGR3IA7ZQ6LWY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JDHXKWUA3VRFLSGR3IA7ZQ6LWY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JDHXKWUA3VRFLSGR3IA7ZQ6LWY/action/storage_attestation","attest_author":"https://pith.science/pith/JDHXKWUA3VRFLSGR3IA7ZQ6LWY/action/author_attestation","sign_citation":"https://pith.science/pith/JDHXKWUA3VRFLSGR3IA7ZQ6LWY/action/citation_signature","submit_replication":"https://pith.science/pith/JDHXKWUA3VRFLSGR3IA7ZQ6LWY/action/replication_record"}},"created_at":"2026-07-05T07:56:30.759743+00:00","updated_at":"2026-07-05T07:56:30.759743+00:00"}