{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:MPZANRAKVZ2326ULM4RHBTAW2B","short_pith_number":"pith:MPZANRAK","schema_version":"1.0","canonical_sha256":"63f206c40aae75bd7a8b672270cc16d0635a0c9469b66f640dae8a87e2054228","source":{"kind":"arxiv","id":"2305.15673","version":1},"attestation_state":"computed","paper":{"title":"BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Aakas Zhiyuli, Xuan Zhang, Xun Liang, Yanfang Chen","submitted_at":"2023-05-25T02:45:22Z","abstract_excerpt":"With the continuous development and change exhibited by large language model (LLM) technology, represented by generative pretrained transformers (GPTs), many classic scenarios in various fields have re-emerged with new opportunities. This paper takes ChatGPT as the modeling object, incorporates LLM technology into the typical book resource understanding and recommendation scenario for the first time, and puts it into practice. By building a ChatGPT-like book recommendation system (BookGPT) framework based on ChatGPT, this paper attempts to apply ChatGPT to recommendation modeling for three typ"},"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":"2305.15673","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2023-05-25T02:45:22Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"8eb5c2d041cb2bcca2b4f2f1dfc2ede7b1f659109591d99d5b6ed0c845717100","abstract_canon_sha256":"201e3f96033336fa994dbc9991a09cf1945664dc996251320acb831f309b6e7b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:22:59.343475Z","signature_b64":"sKtt2+NRD6LFAnYa5R2ISyEYP9AhUgkq3RuJ1WUxmk0O+0ZNkhtzSOQyGtNGDIaF7g8nKwqISw+G/VJN20xqCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63f206c40aae75bd7a8b672270cc16d0635a0c9469b66f640dae8a87e2054228","last_reissued_at":"2026-07-05T09:22:59.343006Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:22:59.343006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Aakas Zhiyuli, Xuan Zhang, Xun Liang, Yanfang Chen","submitted_at":"2023-05-25T02:45:22Z","abstract_excerpt":"With the continuous development and change exhibited by large language model (LLM) technology, represented by generative pretrained transformers (GPTs), many classic scenarios in various fields have re-emerged with new opportunities. This paper takes ChatGPT as the modeling object, incorporates LLM technology into the typical book resource understanding and recommendation scenario for the first time, and puts it into practice. By building a ChatGPT-like book recommendation system (BookGPT) framework based on ChatGPT, this paper attempts to apply ChatGPT to recommendation modeling for three typ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.15673","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/2305.15673/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":"2305.15673","created_at":"2026-07-05T09:22:59.343059+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.15673v1","created_at":"2026-07-05T09:22:59.343059+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.15673","created_at":"2026-07-05T09:22:59.343059+00:00"},{"alias_kind":"pith_short_12","alias_value":"MPZANRAKVZ23","created_at":"2026-07-05T09:22:59.343059+00:00"},{"alias_kind":"pith_short_16","alias_value":"MPZANRAKVZ2326UL","created_at":"2026-07-05T09:22:59.343059+00:00"},{"alias_kind":"pith_short_8","alias_value":"MPZANRAK","created_at":"2026-07-05T09:22:59.343059+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.18665","citing_title":"Membership Inference Attacks on In-Context Examples in LLM-based Recommender Systems","ref_index":55,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MPZANRAKVZ2326ULM4RHBTAW2B","json":"https://pith.science/pith/MPZANRAKVZ2326ULM4RHBTAW2B.json","graph_json":"https://pith.science/api/pith-number/MPZANRAKVZ2326ULM4RHBTAW2B/graph.json","events_json":"https://pith.science/api/pith-number/MPZANRAKVZ2326ULM4RHBTAW2B/events.json","paper":"https://pith.science/paper/MPZANRAK"},"agent_actions":{"view_html":"https://pith.science/pith/MPZANRAKVZ2326ULM4RHBTAW2B","download_json":"https://pith.science/pith/MPZANRAKVZ2326ULM4RHBTAW2B.json","view_paper":"https://pith.science/paper/MPZANRAK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.15673&json=true","fetch_graph":"https://pith.science/api/pith-number/MPZANRAKVZ2326ULM4RHBTAW2B/graph.json","fetch_events":"https://pith.science/api/pith-number/MPZANRAKVZ2326ULM4RHBTAW2B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MPZANRAKVZ2326ULM4RHBTAW2B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MPZANRAKVZ2326ULM4RHBTAW2B/action/storage_attestation","attest_author":"https://pith.science/pith/MPZANRAKVZ2326ULM4RHBTAW2B/action/author_attestation","sign_citation":"https://pith.science/pith/MPZANRAKVZ2326ULM4RHBTAW2B/action/citation_signature","submit_replication":"https://pith.science/pith/MPZANRAKVZ2326ULM4RHBTAW2B/action/replication_record"}},"created_at":"2026-07-05T09:22:59.343059+00:00","updated_at":"2026-07-05T09:22:59.343059+00:00"}