{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:SZYYRFEVQHGHFQWNUPIKI26RJE","short_pith_number":"pith:SZYYRFEV","schema_version":"1.0","canonical_sha256":"967188949581cc72c2cda3d0a46bd1492695efd3cf81212eb829563829875eab","source":{"kind":"arxiv","id":"1805.02971","version":2},"attestation_state":"computed","paper":{"title":"Multinomial Logit Bandit with Linear Utility Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Mingdong Ou, Nan Li, Rong Jin, Shenghuo Zhu","submitted_at":"2018-05-08T12:23:54Z","abstract_excerpt":"Multinomial logit bandit is a sequential subset selection problem which arises in many applications. In each round, the player selects a $K$-cardinality subset from $N$ candidate items, and receives a reward which is governed by a {\\it multinomial logit} (MNL) choice model considering both item utility and substitution property among items. The player's objective is to dynamically learn the parameters of MNL model and maximize cumulative reward over a finite horizon $T$. This problem faces the exploration-exploitation dilemma, and the involved combinatorial nature makes it non-trivial. In rece"},"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":"1805.02971","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-08T12:23:54Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"fd9bff22f35ef100f75c4c3d9554a329b5e55ad6a525f1476f7a618a631896ef","abstract_canon_sha256":"c838ddf92b0f750dcb855af7b7ed3093bb6a1c359f4eb5070f0454406f7100aa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:18.381227Z","signature_b64":"h7n6/HHLX54Z3dH0Ap37542SQY3br2rSpvJ84gmROUYkkrlUpQGBIsVQIcT0TC7B4J3C2ku5tkXt1Msn/SrWDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"967188949581cc72c2cda3d0a46bd1492695efd3cf81212eb829563829875eab","last_reissued_at":"2026-05-17T23:52:18.380652Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:18.380652Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multinomial Logit Bandit with Linear Utility Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Mingdong Ou, Nan Li, Rong Jin, Shenghuo Zhu","submitted_at":"2018-05-08T12:23:54Z","abstract_excerpt":"Multinomial logit bandit is a sequential subset selection problem which arises in many applications. In each round, the player selects a $K$-cardinality subset from $N$ candidate items, and receives a reward which is governed by a {\\it multinomial logit} (MNL) choice model considering both item utility and substitution property among items. The player's objective is to dynamically learn the parameters of MNL model and maximize cumulative reward over a finite horizon $T$. This problem faces the exploration-exploitation dilemma, and the involved combinatorial nature makes it non-trivial. In rece"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.02971","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1805.02971","created_at":"2026-05-17T23:52:18.380761+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.02971v2","created_at":"2026-05-17T23:52:18.380761+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.02971","created_at":"2026-05-17T23:52:18.380761+00:00"},{"alias_kind":"pith_short_12","alias_value":"SZYYRFEVQHGH","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"SZYYRFEVQHGHFQWN","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"SZYYRFEV","created_at":"2026-05-18T12:32:53.628368+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.19008","citing_title":"Optimal Online and Offline Algorithms for Contextual MNL with Applications to Assortment and Pricing","ref_index":90,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SZYYRFEVQHGHFQWNUPIKI26RJE","json":"https://pith.science/pith/SZYYRFEVQHGHFQWNUPIKI26RJE.json","graph_json":"https://pith.science/api/pith-number/SZYYRFEVQHGHFQWNUPIKI26RJE/graph.json","events_json":"https://pith.science/api/pith-number/SZYYRFEVQHGHFQWNUPIKI26RJE/events.json","paper":"https://pith.science/paper/SZYYRFEV"},"agent_actions":{"view_html":"https://pith.science/pith/SZYYRFEVQHGHFQWNUPIKI26RJE","download_json":"https://pith.science/pith/SZYYRFEVQHGHFQWNUPIKI26RJE.json","view_paper":"https://pith.science/paper/SZYYRFEV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.02971&json=true","fetch_graph":"https://pith.science/api/pith-number/SZYYRFEVQHGHFQWNUPIKI26RJE/graph.json","fetch_events":"https://pith.science/api/pith-number/SZYYRFEVQHGHFQWNUPIKI26RJE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SZYYRFEVQHGHFQWNUPIKI26RJE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SZYYRFEVQHGHFQWNUPIKI26RJE/action/storage_attestation","attest_author":"https://pith.science/pith/SZYYRFEVQHGHFQWNUPIKI26RJE/action/author_attestation","sign_citation":"https://pith.science/pith/SZYYRFEVQHGHFQWNUPIKI26RJE/action/citation_signature","submit_replication":"https://pith.science/pith/SZYYRFEVQHGHFQWNUPIKI26RJE/action/replication_record"}},"created_at":"2026-05-17T23:52:18.380761+00:00","updated_at":"2026-05-17T23:52:18.380761+00:00"}