{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5HIEB3TBL4LRGI35GZWFDIINSJ","short_pith_number":"pith:5HIEB3TB","schema_version":"1.0","canonical_sha256":"e9d040ee615f1713237d366c51a10d9262f9a0eae740ab458385cd5a5c830bbf","source":{"kind":"arxiv","id":"2307.02157","version":1},"attestation_state":"computed","paper":{"title":"Generative Job Recommendations with Large Language Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Hengshu Zhu, Hui Xiong, Likang Wu, Xiao Hu, Zhaopeng Qiu, Zhi Zheng","submitted_at":"2023-07-05T09:58:08Z","abstract_excerpt":"The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process. Predominantly, current job recommendations deploy either collaborative filtering or person-job matching strategies. However, these models tend to operate as \"black-box\" systems and lack the capacity to offer explainable guidance to job seekers. Moreover, conventional matching-based recommendation methods are limited to retrieving and ranking existing jobs in the database, restricting their potential as comprehensive career AI advisors. To this end, h"},"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":"2307.02157","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-07-05T09:58:08Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"cd9a907251d452742a3892531bc170bcd6b38892a43a57239d7ae103db4b4839","abstract_canon_sha256":"6f29585c90ef5c2439a29364205454afa0061f659c0bf4ffee82e40524c0da95"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:28:05.772035Z","signature_b64":"JBkQ2ubt61x21XCuOs0trADU2lWund02elvTPRk3AGsvVr3gDAuXjbpcyTCucbKNNiMhvEHNc3VMp9hLpbAZAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e9d040ee615f1713237d366c51a10d9262f9a0eae740ab458385cd5a5c830bbf","last_reissued_at":"2026-07-05T06:28:05.771590Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:28:05.771590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generative Job Recommendations with Large Language Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Hengshu Zhu, Hui Xiong, Likang Wu, Xiao Hu, Zhaopeng Qiu, Zhi Zheng","submitted_at":"2023-07-05T09:58:08Z","abstract_excerpt":"The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process. Predominantly, current job recommendations deploy either collaborative filtering or person-job matching strategies. However, these models tend to operate as \"black-box\" systems and lack the capacity to offer explainable guidance to job seekers. Moreover, conventional matching-based recommendation methods are limited to retrieving and ranking existing jobs in the database, restricting their potential as comprehensive career AI advisors. To this end, h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.02157","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/2307.02157/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":"2307.02157","created_at":"2026-07-05T06:28:05.771654+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.02157v1","created_at":"2026-07-05T06:28:05.771654+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.02157","created_at":"2026-07-05T06:28:05.771654+00:00"},{"alias_kind":"pith_short_12","alias_value":"5HIEB3TBL4LR","created_at":"2026-07-05T06:28:05.771654+00:00"},{"alias_kind":"pith_short_16","alias_value":"5HIEB3TBL4LRGI35","created_at":"2026-07-05T06:28:05.771654+00:00"},{"alias_kind":"pith_short_8","alias_value":"5HIEB3TB","created_at":"2026-07-05T06:28:05.771654+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.03131","citing_title":"RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation","ref_index":52,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5HIEB3TBL4LRGI35GZWFDIINSJ","json":"https://pith.science/pith/5HIEB3TBL4LRGI35GZWFDIINSJ.json","graph_json":"https://pith.science/api/pith-number/5HIEB3TBL4LRGI35GZWFDIINSJ/graph.json","events_json":"https://pith.science/api/pith-number/5HIEB3TBL4LRGI35GZWFDIINSJ/events.json","paper":"https://pith.science/paper/5HIEB3TB"},"agent_actions":{"view_html":"https://pith.science/pith/5HIEB3TBL4LRGI35GZWFDIINSJ","download_json":"https://pith.science/pith/5HIEB3TBL4LRGI35GZWFDIINSJ.json","view_paper":"https://pith.science/paper/5HIEB3TB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.02157&json=true","fetch_graph":"https://pith.science/api/pith-number/5HIEB3TBL4LRGI35GZWFDIINSJ/graph.json","fetch_events":"https://pith.science/api/pith-number/5HIEB3TBL4LRGI35GZWFDIINSJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5HIEB3TBL4LRGI35GZWFDIINSJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5HIEB3TBL4LRGI35GZWFDIINSJ/action/storage_attestation","attest_author":"https://pith.science/pith/5HIEB3TBL4LRGI35GZWFDIINSJ/action/author_attestation","sign_citation":"https://pith.science/pith/5HIEB3TBL4LRGI35GZWFDIINSJ/action/citation_signature","submit_replication":"https://pith.science/pith/5HIEB3TBL4LRGI35GZWFDIINSJ/action/replication_record"}},"created_at":"2026-07-05T06:28:05.771654+00:00","updated_at":"2026-07-05T06:28:05.771654+00:00"}