{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:24QSVFJN4YFDDAVMXTS237WHHJ","short_pith_number":"pith:24QSVFJN","schema_version":"1.0","canonical_sha256":"d7212a952de60a3182acbce5adfec73a41c45586f0744df29537496a46873e27","source":{"kind":"arxiv","id":"2409.17460","version":1},"attestation_state":"computed","paper":{"title":"Towards More Relevant Product Search Ranking Via Large Language Models: An Empirical Study","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Atul Singh, Cun Mu, Jingbo Liu, Qi Liu, Zheng Yan","submitted_at":"2024-09-26T01:38:05Z","abstract_excerpt":"Training Learning-to-Rank models for e-commerce product search ranking can be challenging due to the lack of a gold standard of ranking relevance. In this paper, we decompose ranking relevance into content-based and engagement-based aspects, and we propose to leverage Large Language Models (LLMs) for both label and feature generation in model training, primarily aiming to improve the model's predictive capability for content-based relevance. Additionally, we introduce different sigmoid transformations on the LLM outputs to polarize relevance scores in labeling, enhancing the model's ability to"},"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":"2409.17460","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-09-26T01:38:05Z","cross_cats_sorted":[],"title_canon_sha256":"73b28a9d07ec4de4f711be7d755aebf6c26f2507ccab843dc588b5bbea67e8f8","abstract_canon_sha256":"c8a96a137714610f1f272451a2bcf2bb56abe5efe96c3d502668cc8b611526da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:11:54.298455Z","signature_b64":"VgL2+E7/TRxq+eeCLFNWDXTareDwODfOWMVKaHDfqPbEOmthwyQG2PTAQy3FQjDZbC0QPKufPu1yN5KOX6wIDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7212a952de60a3182acbce5adfec73a41c45586f0744df29537496a46873e27","last_reissued_at":"2026-07-05T09:11:54.297931Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:11:54.297931Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards More Relevant Product Search Ranking Via Large Language Models: An Empirical Study","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Atul Singh, Cun Mu, Jingbo Liu, Qi Liu, Zheng Yan","submitted_at":"2024-09-26T01:38:05Z","abstract_excerpt":"Training Learning-to-Rank models for e-commerce product search ranking can be challenging due to the lack of a gold standard of ranking relevance. In this paper, we decompose ranking relevance into content-based and engagement-based aspects, and we propose to leverage Large Language Models (LLMs) for both label and feature generation in model training, primarily aiming to improve the model's predictive capability for content-based relevance. Additionally, we introduce different sigmoid transformations on the LLM outputs to polarize relevance scores in labeling, enhancing the model's ability to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.17460","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/2409.17460/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":"2409.17460","created_at":"2026-07-05T09:11:54.298006+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.17460v1","created_at":"2026-07-05T09:11:54.298006+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.17460","created_at":"2026-07-05T09:11:54.298006+00:00"},{"alias_kind":"pith_short_12","alias_value":"24QSVFJN4YFD","created_at":"2026-07-05T09:11:54.298006+00:00"},{"alias_kind":"pith_short_16","alias_value":"24QSVFJN4YFDDAVM","created_at":"2026-07-05T09:11:54.298006+00:00"},{"alias_kind":"pith_short_8","alias_value":"24QSVFJN","created_at":"2026-07-05T09:11:54.298006+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.27704","citing_title":"Joint Optimization of Relevance and Engagement in Multi-Task Ranking for E-Commerce with Efficient LLM Supervision","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/24QSVFJN4YFDDAVMXTS237WHHJ","json":"https://pith.science/pith/24QSVFJN4YFDDAVMXTS237WHHJ.json","graph_json":"https://pith.science/api/pith-number/24QSVFJN4YFDDAVMXTS237WHHJ/graph.json","events_json":"https://pith.science/api/pith-number/24QSVFJN4YFDDAVMXTS237WHHJ/events.json","paper":"https://pith.science/paper/24QSVFJN"},"agent_actions":{"view_html":"https://pith.science/pith/24QSVFJN4YFDDAVMXTS237WHHJ","download_json":"https://pith.science/pith/24QSVFJN4YFDDAVMXTS237WHHJ.json","view_paper":"https://pith.science/paper/24QSVFJN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.17460&json=true","fetch_graph":"https://pith.science/api/pith-number/24QSVFJN4YFDDAVMXTS237WHHJ/graph.json","fetch_events":"https://pith.science/api/pith-number/24QSVFJN4YFDDAVMXTS237WHHJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/24QSVFJN4YFDDAVMXTS237WHHJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/24QSVFJN4YFDDAVMXTS237WHHJ/action/storage_attestation","attest_author":"https://pith.science/pith/24QSVFJN4YFDDAVMXTS237WHHJ/action/author_attestation","sign_citation":"https://pith.science/pith/24QSVFJN4YFDDAVMXTS237WHHJ/action/citation_signature","submit_replication":"https://pith.science/pith/24QSVFJN4YFDDAVMXTS237WHHJ/action/replication_record"}},"created_at":"2026-07-05T09:11:54.298006+00:00","updated_at":"2026-07-05T09:11:54.298006+00:00"}