{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EE6UZTMWTANSKCKRH4AWHG4NGJ","short_pith_number":"pith:EE6UZTMW","schema_version":"1.0","canonical_sha256":"213d4ccd96981b2509513f01639b8d3276f26ebb9c9393a14347bc868f4f6098","source":{"kind":"arxiv","id":"2406.12433","version":4},"attestation_state":"computed","paper":{"title":"LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Bo Chen, Huifeng Guo, Jingtong Gao, Ruiming Tang, Wanyu Wang, Weiwen Liu, Xiangyang Li, Xiangyu Zhao, Yichao Wang","submitted_at":"2024-06-18T09:29:18Z","abstract_excerpt":"Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms. Traditional reranking models have focused predominantly on accuracy, but modern applications demand consideration of additional criteria such as diversity and fairness. Existing reranking approaches often fail to harmonize these diverse criteria effectively at the model level. Moreover, these models frequently encounter challenges with scalability and personalization due to their complexity and the varying significance of different reranking criteria in diver"},"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":"2406.12433","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-06-18T09:29:18Z","cross_cats_sorted":[],"title_canon_sha256":"8c4dc6594d2e3fa96647ed9474855d763eaf93b7d78e97136db45627f628e5dd","abstract_canon_sha256":"0a0853fb42c580db37a71485c88cc3a529112c905b2a08f074ae931a6af18f7b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:08:18.457380Z","signature_b64":"NM5bJtlOEeOf7fldwPU4qSylUJY3S93bP8hlnvs8RmoEJ+Y46a9WzXKXBo9ptoQlMhMrdSf5LiB8zpzqMYOlBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"213d4ccd96981b2509513f01639b8d3276f26ebb9c9393a14347bc868f4f6098","last_reissued_at":"2026-07-05T10:08:18.456941Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:08:18.456941Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Bo Chen, Huifeng Guo, Jingtong Gao, Ruiming Tang, Wanyu Wang, Weiwen Liu, Xiangyang Li, Xiangyu Zhao, Yichao Wang","submitted_at":"2024-06-18T09:29:18Z","abstract_excerpt":"Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms. Traditional reranking models have focused predominantly on accuracy, but modern applications demand consideration of additional criteria such as diversity and fairness. Existing reranking approaches often fail to harmonize these diverse criteria effectively at the model level. Moreover, these models frequently encounter challenges with scalability and personalization due to their complexity and the varying significance of different reranking criteria in diver"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.12433","kind":"arxiv","version":4},"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/2406.12433/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":"2406.12433","created_at":"2026-07-05T10:08:18.456998+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.12433v4","created_at":"2026-07-05T10:08:18.456998+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.12433","created_at":"2026-07-05T10:08:18.456998+00:00"},{"alias_kind":"pith_short_12","alias_value":"EE6UZTMWTANS","created_at":"2026-07-05T10:08:18.456998+00:00"},{"alias_kind":"pith_short_16","alias_value":"EE6UZTMWTANSKCKR","created_at":"2026-07-05T10:08:18.456998+00:00"},{"alias_kind":"pith_short_8","alias_value":"EE6UZTMW","created_at":"2026-07-05T10:08:18.456998+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04535","citing_title":"Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27865","citing_title":"From Bootstrapping to Sequence Modeling: A Unified Generative Framework for Personalized Landing-Page Modeling","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31693","citing_title":"ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping","ref_index":66,"is_internal_anchor":false},{"citing_arxiv_id":"2606.00540","citing_title":"Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges","ref_index":61,"is_internal_anchor":false},{"citing_arxiv_id":"2409.14634","citing_title":"Human-LLM Compound System for Scientific Ideation through Facet Recombination and Novelty Evaluation","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2604.27599","citing_title":"One Pass, Any Order: Position-Invariant Listwise Reranking for LLM-Based Recommendation","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EE6UZTMWTANSKCKRH4AWHG4NGJ","json":"https://pith.science/pith/EE6UZTMWTANSKCKRH4AWHG4NGJ.json","graph_json":"https://pith.science/api/pith-number/EE6UZTMWTANSKCKRH4AWHG4NGJ/graph.json","events_json":"https://pith.science/api/pith-number/EE6UZTMWTANSKCKRH4AWHG4NGJ/events.json","paper":"https://pith.science/paper/EE6UZTMW"},"agent_actions":{"view_html":"https://pith.science/pith/EE6UZTMWTANSKCKRH4AWHG4NGJ","download_json":"https://pith.science/pith/EE6UZTMWTANSKCKRH4AWHG4NGJ.json","view_paper":"https://pith.science/paper/EE6UZTMW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.12433&json=true","fetch_graph":"https://pith.science/api/pith-number/EE6UZTMWTANSKCKRH4AWHG4NGJ/graph.json","fetch_events":"https://pith.science/api/pith-number/EE6UZTMWTANSKCKRH4AWHG4NGJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EE6UZTMWTANSKCKRH4AWHG4NGJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EE6UZTMWTANSKCKRH4AWHG4NGJ/action/storage_attestation","attest_author":"https://pith.science/pith/EE6UZTMWTANSKCKRH4AWHG4NGJ/action/author_attestation","sign_citation":"https://pith.science/pith/EE6UZTMWTANSKCKRH4AWHG4NGJ/action/citation_signature","submit_replication":"https://pith.science/pith/EE6UZTMWTANSKCKRH4AWHG4NGJ/action/replication_record"}},"created_at":"2026-07-05T10:08:18.456998+00:00","updated_at":"2026-07-05T10:08:18.456998+00:00"}