{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WKCT3GBS3JAHZSL2DVVIBP6QLS","short_pith_number":"pith:WKCT3GBS","schema_version":"1.0","canonical_sha256":"b2853d9832da407cc97a1d6a80bfd05ca382a98cc9e340e670c5465dad4454bf","source":{"kind":"arxiv","id":"2605.11118","version":2},"attestation_state":"computed","paper":{"title":"A Cascaded Generative Approach for e-Commerce Recommendations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Cascaded generative models for themes and keywords deliver 2.7% higher cart adds in e-commerce storefronts.","cross_cats":["cs.IR"],"primary_cat":"cs.AI","authors_text":"Guanghua Shu, Hamidreza Shahidi, Moein Hasani, Tejaswi Tenneti, Trace Levinson, Vinesh Gudla, Yuan Zhong","submitted_at":"2026-05-11T18:27:08Z","abstract_excerpt":"Personalized storefronts in large e-commerce marketplaces are often assembled from many independent components: static themes per page section (\"placement\"), retrieval systems to fetch eligible products per placement, and pointwise rankers to order content. While effective in optimizing for aggregate preferences, this paradigm is rigid and can limit personalization and semantic cohesion across the page. This makes it poorly suited to support dynamic objectives and merchandising requirements over time. To address this, we introduce a cascaded merchandising framework that decomposes storefront c"},"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":true,"formal_links_present":true},"canonical_record":{"source":{"id":"2605.11118","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-11T18:27:08Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"903e6aa7d1c7938c6eabff486b75a022336673c2726f20d4de0e8df31ae1b4b2","abstract_canon_sha256":"363756637a276fe9fee8a5e0db69b2c61a5a6928d063f2d18d0b54797983ce46"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:00:42.714753Z","signature_b64":"oq7yEMJORJw9qpE7ONdzRs/qMNrQIlF0qM39kTQjApC/saNij4nfo07bHx1N/gDfc1PKNSMhczTA0LjwXBK9Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2853d9832da407cc97a1d6a80bfd05ca382a98cc9e340e670c5465dad4454bf","last_reissued_at":"2026-05-20T00:00:42.714029Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:00:42.714029Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Cascaded Generative Approach for e-Commerce Recommendations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Cascaded generative models for themes and keywords deliver 2.7% higher cart adds in e-commerce storefronts.","cross_cats":["cs.IR"],"primary_cat":"cs.AI","authors_text":"Guanghua Shu, Hamidreza Shahidi, Moein Hasani, Tejaswi Tenneti, Trace Levinson, Vinesh Gudla, Yuan Zhong","submitted_at":"2026-05-11T18:27:08Z","abstract_excerpt":"Personalized storefronts in large e-commerce marketplaces are often assembled from many independent components: static themes per page section (\"placement\"), retrieval systems to fetch eligible products per placement, and pointwise rankers to order content. While effective in optimizing for aggregate preferences, this paradigm is rigid and can limit personalization and semantic cohesion across the page. This makes it poorly suited to support dynamic objectives and merchandising requirements over time. To address this, we introduce a cascaded merchandising framework that decomposes storefront c"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"In online experiments, this framework yields an estimated +2.7% lift in cart adds per page view over a strong baseline.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The teacher-student fine-tuned generative models can reliably produce high-quality, safe, and semantically cohesive themes and keywords that integrate effectively with traditional ranking models under production latency and cost limits.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A cascaded generative merchandising framework with placement theme generation, constrained keyword generation, and teacher-student fine-tuning achieves a 2.7% lift in cart adds per page view over a strong baseline in online e-commerce experiments.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Cascaded generative models for themes and keywords deliver 2.7% higher cart adds in e-commerce storefronts.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"de92fcfc03f1da667c4105ac75b164dc270f02dc447084cfeb7257593827bc7e"},"source":{"id":"2605.11118","kind":"arxiv","version":2},"verdict":{"id":"e302f195-e6e4-4376-ba68-1b3a8bb80522","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T14:34:37.252981Z","strongest_claim":"In online experiments, this framework yields an estimated +2.7% lift in cart adds per page view over a strong baseline.","one_line_summary":"A cascaded generative merchandising framework with placement theme generation, constrained keyword generation, and teacher-student fine-tuning achieves a 2.7% lift in cart adds per page view over a strong baseline in online e-commerce experiments.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The teacher-student fine-tuned generative models can reliably produce high-quality, safe, and semantically cohesive themes and keywords that integrate effectively with traditional ranking models under production latency and cost limits.","pith_extraction_headline":"Cascaded generative models for themes and keywords deliver 2.7% higher cart adds in e-commerce storefronts."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.11118/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:35:53.177838Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T10:31:16.805176Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T08:46:43.168328Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"d6415c994cf2d69949ac6f6c05551be121ea23d2b0f4d549343dbf75e574d1eb"},"references":{"count":19,"sample":[{"doi":"","year":2025,"title":"Francesco Fabbri, Gustavo Penha, Edoardo D’Amico, Alice Wang, Marco De Nadai, Jackie Doremus, Paul Gigioli, Andreas Damianou, Oskar Stål, and Mounia Lalmas. 2025. Evaluating Podcast Recommendations wi","work_id":"d116f793-0a58-446a-8642-3e04ac5b1519","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Retrieval-Augmented Generation for Large Language Models: A Survey","work_id":"b80d2790-6cd9-4c87-b3c4-de404f99a80e","ref_index":2,"cited_arxiv_id":"2312.10997","is_internal_anchor":true},{"doi":"","year":2024,"title":"The Llama 3 Herd of Models","work_id":"1549a635-88af-4ac1-acfe-51ae7bb53345","ref_index":3,"cited_arxiv_id":"2407.21783","is_internal_anchor":true},{"doi":"","year":2021,"title":"DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing","work_id":"480bf7dd-7121-4d1f-8b53-7cc6ce479835","ref_index":4,"cited_arxiv_id":"2111.09543","is_internal_anchor":true},{"doi":"","year":2015,"title":"Distilling the Knowledge in a Neural Network","work_id":"d927ab1f-17b8-4002-9d09-c3d55764fbad","ref_index":5,"cited_arxiv_id":"1503.02531","is_internal_anchor":true}],"resolved_work":19,"snapshot_sha256":"48bd35b3fde60cea8d878a3d502ae95c875d11e8cb11347db21fdcdf018d1105","internal_anchors":10},"formal_canon":{"evidence_count":2,"snapshot_sha256":"17579c8e27f67dfea0896507b2f1875fe76b0dd166afa8e6e7634567d8594b30"},"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":"2605.11118","created_at":"2026-05-20T00:00:42.714138+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.11118v2","created_at":"2026-05-20T00:00:42.714138+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.11118","created_at":"2026-05-20T00:00:42.714138+00:00"},{"alias_kind":"pith_short_12","alias_value":"WKCT3GBS3JAH","created_at":"2026-05-20T00:00:42.714138+00:00"},{"alias_kind":"pith_short_16","alias_value":"WKCT3GBS3JAHZSL2","created_at":"2026-05-20T00:00:42.714138+00:00"},{"alias_kind":"pith_short_8","alias_value":"WKCT3GBS","created_at":"2026-05-20T00:00:42.714138+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WKCT3GBS3JAHZSL2DVVIBP6QLS","json":"https://pith.science/pith/WKCT3GBS3JAHZSL2DVVIBP6QLS.json","graph_json":"https://pith.science/api/pith-number/WKCT3GBS3JAHZSL2DVVIBP6QLS/graph.json","events_json":"https://pith.science/api/pith-number/WKCT3GBS3JAHZSL2DVVIBP6QLS/events.json","paper":"https://pith.science/paper/WKCT3GBS"},"agent_actions":{"view_html":"https://pith.science/pith/WKCT3GBS3JAHZSL2DVVIBP6QLS","download_json":"https://pith.science/pith/WKCT3GBS3JAHZSL2DVVIBP6QLS.json","view_paper":"https://pith.science/paper/WKCT3GBS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.11118&json=true","fetch_graph":"https://pith.science/api/pith-number/WKCT3GBS3JAHZSL2DVVIBP6QLS/graph.json","fetch_events":"https://pith.science/api/pith-number/WKCT3GBS3JAHZSL2DVVIBP6QLS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WKCT3GBS3JAHZSL2DVVIBP6QLS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WKCT3GBS3JAHZSL2DVVIBP6QLS/action/storage_attestation","attest_author":"https://pith.science/pith/WKCT3GBS3JAHZSL2DVVIBP6QLS/action/author_attestation","sign_citation":"https://pith.science/pith/WKCT3GBS3JAHZSL2DVVIBP6QLS/action/citation_signature","submit_replication":"https://pith.science/pith/WKCT3GBS3JAHZSL2DVVIBP6QLS/action/replication_record"}},"created_at":"2026-05-20T00:00:42.714138+00:00","updated_at":"2026-05-20T00:00:42.714138+00:00"}