{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:U6X6HYLP5UT53TFQN3MS7TPWPC","short_pith_number":"pith:U6X6HYLP","schema_version":"1.0","canonical_sha256":"a7afe3e16fed27ddccb06ed92fcdf678b7a48815f1e71138535f802320a1940c","source":{"kind":"arxiv","id":"2402.18917","version":1},"attestation_state":"computed","paper":{"title":"Stop Relying on No-Choice and Do not Repeat the Moves: Optimal, Efficient and Practical Algorithms for Assortment Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.LG","authors_text":"Aadirupa Saha, Pierre Gaillard","submitted_at":"2024-02-29T07:17:04Z","abstract_excerpt":"We address the problem of active online assortment optimization problem with preference feedback, which is a framework for modeling user choices and subsetwise utility maximization. The framework is useful in various real-world applications including ad placement, online retail, recommender systems, fine-tuning language models, amongst many. The problem, although has been studied in the past, lacks an intuitive and practical solution approach with simultaneously efficient algorithm and optimal regret guarantee. E.g., popularly used assortment selection algorithms often require the presence of "},"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":"2402.18917","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-29T07:17:04Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"b63c252fab8471e15e6e58a657ed9b2c86ac2e8c8b2db8b15d8b2ed9b4b4c04b","abstract_canon_sha256":"cabbe9e942e2291a935f97fbbd5ae33abba8b2c0768e40c50647e590eb720836"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:50:29.827327Z","signature_b64":"JD3wcGn85+oKY49eBORCHcIELI7kSjBtvWdS7pbesWXX/TLFizF/uc3GLEA87uhsfuYeGO/KyI5uojVRkSgPCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7afe3e16fed27ddccb06ed92fcdf678b7a48815f1e71138535f802320a1940c","last_reissued_at":"2026-07-05T07:50:29.826799Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:50:29.826799Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stop Relying on No-Choice and Do not Repeat the Moves: Optimal, Efficient and Practical Algorithms for Assortment Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.LG","authors_text":"Aadirupa Saha, Pierre Gaillard","submitted_at":"2024-02-29T07:17:04Z","abstract_excerpt":"We address the problem of active online assortment optimization problem with preference feedback, which is a framework for modeling user choices and subsetwise utility maximization. The framework is useful in various real-world applications including ad placement, online retail, recommender systems, fine-tuning language models, amongst many. The problem, although has been studied in the past, lacks an intuitive and practical solution approach with simultaneously efficient algorithm and optimal regret guarantee. E.g., popularly used assortment selection algorithms often require the presence of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.18917","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/2402.18917/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":"2402.18917","created_at":"2026-07-05T07:50:29.826852+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.18917v1","created_at":"2026-07-05T07:50:29.826852+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.18917","created_at":"2026-07-05T07:50:29.826852+00:00"},{"alias_kind":"pith_short_12","alias_value":"U6X6HYLP5UT5","created_at":"2026-07-05T07:50:29.826852+00:00"},{"alias_kind":"pith_short_16","alias_value":"U6X6HYLP5UT53TFQ","created_at":"2026-07-05T07:50:29.826852+00:00"},{"alias_kind":"pith_short_8","alias_value":"U6X6HYLP","created_at":"2026-07-05T07:50:29.826852+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.17238","citing_title":"Learning in Position-Aware Multinomial Logit Bandits: From Multiplicative to General Position Effects","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19008","citing_title":"Optimal Online and Offline Algorithms for Contextual MNL with Applications to Assortment and Pricing","ref_index":101,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U6X6HYLP5UT53TFQN3MS7TPWPC","json":"https://pith.science/pith/U6X6HYLP5UT53TFQN3MS7TPWPC.json","graph_json":"https://pith.science/api/pith-number/U6X6HYLP5UT53TFQN3MS7TPWPC/graph.json","events_json":"https://pith.science/api/pith-number/U6X6HYLP5UT53TFQN3MS7TPWPC/events.json","paper":"https://pith.science/paper/U6X6HYLP"},"agent_actions":{"view_html":"https://pith.science/pith/U6X6HYLP5UT53TFQN3MS7TPWPC","download_json":"https://pith.science/pith/U6X6HYLP5UT53TFQN3MS7TPWPC.json","view_paper":"https://pith.science/paper/U6X6HYLP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.18917&json=true","fetch_graph":"https://pith.science/api/pith-number/U6X6HYLP5UT53TFQN3MS7TPWPC/graph.json","fetch_events":"https://pith.science/api/pith-number/U6X6HYLP5UT53TFQN3MS7TPWPC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U6X6HYLP5UT53TFQN3MS7TPWPC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U6X6HYLP5UT53TFQN3MS7TPWPC/action/storage_attestation","attest_author":"https://pith.science/pith/U6X6HYLP5UT53TFQN3MS7TPWPC/action/author_attestation","sign_citation":"https://pith.science/pith/U6X6HYLP5UT53TFQN3MS7TPWPC/action/citation_signature","submit_replication":"https://pith.science/pith/U6X6HYLP5UT53TFQN3MS7TPWPC/action/replication_record"}},"created_at":"2026-07-05T07:50:29.826852+00:00","updated_at":"2026-07-05T07:50:29.826852+00:00"}