{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DXHX4XNDO7NKEOAWU22OWFXCJH","short_pith_number":"pith:DXHX4XND","schema_version":"1.0","canonical_sha256":"1dcf7e5da377daa23816a6b4eb16e249f60d622ca47957476a77fcd9391932b0","source":{"kind":"arxiv","id":"2307.13656","version":3},"attestation_state":"computed","paper":{"title":"Assortment Optimization with Visibility Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Andrea Lodi, Danny Segev, Marouane Ibn Brahim, Omar El Housni, Theo Barre","submitted_at":"2023-07-25T17:05:17Z","abstract_excerpt":"Motivated by applications in e-retail and online advertising, we study the problem of assortment optimization under visibility constraints, that we refer to as APV. Here, we are given a universe of substitutable products and a stream of customers. The objective is to determine the optimal assortment of products to offer to each customer in order to maximize the total expected revenue, subject to exogenously-given visibility constraints, stating that each product should be shown to a minimum number of customers. We assume that customer choices follow a Multinomial Logit model (MNL). We provide "},"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":"2307.13656","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2023-07-25T17:05:17Z","cross_cats_sorted":[],"title_canon_sha256":"e0a883353d61b7aa2f27d05edebcac7b3a11c57c74b07252ed869c36f18856cf","abstract_canon_sha256":"03de2301031915d051434ad1d3201a3e5b682ced78b5f9f58818666b89623e4a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:14:36.069398Z","signature_b64":"FfDJIMqmeiQprHkpJ6aLGDg5WFwyNiHGw3fyyulZo2DmHe4CTCOGn6fs5VgQX5gmxpHKH/t0hPzxbhaZngzXBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1dcf7e5da377daa23816a6b4eb16e249f60d622ca47957476a77fcd9391932b0","last_reissued_at":"2026-07-05T10:14:36.068933Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:14:36.068933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Assortment Optimization with Visibility Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Andrea Lodi, Danny Segev, Marouane Ibn Brahim, Omar El Housni, Theo Barre","submitted_at":"2023-07-25T17:05:17Z","abstract_excerpt":"Motivated by applications in e-retail and online advertising, we study the problem of assortment optimization under visibility constraints, that we refer to as APV. Here, we are given a universe of substitutable products and a stream of customers. The objective is to determine the optimal assortment of products to offer to each customer in order to maximize the total expected revenue, subject to exogenously-given visibility constraints, stating that each product should be shown to a minimum number of customers. We assume that customer choices follow a Multinomial Logit model (MNL). We provide "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.13656","kind":"arxiv","version":3},"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/2307.13656/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":"2307.13656","created_at":"2026-07-05T10:14:36.068983+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.13656v3","created_at":"2026-07-05T10:14:36.068983+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.13656","created_at":"2026-07-05T10:14:36.068983+00:00"},{"alias_kind":"pith_short_12","alias_value":"DXHX4XNDO7NK","created_at":"2026-07-05T10:14:36.068983+00:00"},{"alias_kind":"pith_short_16","alias_value":"DXHX4XNDO7NKEOAW","created_at":"2026-07-05T10:14:36.068983+00:00"},{"alias_kind":"pith_short_8","alias_value":"DXHX4XND","created_at":"2026-07-05T10:14:36.068983+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2403.08929","citing_title":"Two-sided Assortment Optimization: Adaptivity Gaps and Approximation Algorithms","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DXHX4XNDO7NKEOAWU22OWFXCJH","json":"https://pith.science/pith/DXHX4XNDO7NKEOAWU22OWFXCJH.json","graph_json":"https://pith.science/api/pith-number/DXHX4XNDO7NKEOAWU22OWFXCJH/graph.json","events_json":"https://pith.science/api/pith-number/DXHX4XNDO7NKEOAWU22OWFXCJH/events.json","paper":"https://pith.science/paper/DXHX4XND"},"agent_actions":{"view_html":"https://pith.science/pith/DXHX4XNDO7NKEOAWU22OWFXCJH","download_json":"https://pith.science/pith/DXHX4XNDO7NKEOAWU22OWFXCJH.json","view_paper":"https://pith.science/paper/DXHX4XND","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.13656&json=true","fetch_graph":"https://pith.science/api/pith-number/DXHX4XNDO7NKEOAWU22OWFXCJH/graph.json","fetch_events":"https://pith.science/api/pith-number/DXHX4XNDO7NKEOAWU22OWFXCJH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DXHX4XNDO7NKEOAWU22OWFXCJH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DXHX4XNDO7NKEOAWU22OWFXCJH/action/storage_attestation","attest_author":"https://pith.science/pith/DXHX4XNDO7NKEOAWU22OWFXCJH/action/author_attestation","sign_citation":"https://pith.science/pith/DXHX4XNDO7NKEOAWU22OWFXCJH/action/citation_signature","submit_replication":"https://pith.science/pith/DXHX4XNDO7NKEOAWU22OWFXCJH/action/replication_record"}},"created_at":"2026-07-05T10:14:36.068983+00:00","updated_at":"2026-07-05T10:14:36.068983+00:00"}