{"id":"W3089129971","doi":"10.1287/ijoo.2019.0037","title":"A Partially Ranked Choice Model for Large-Scale Data-Driven Assortment Optimization","year":2020,"lang":"en","type":"article","venue":"INFORMS Journal on Optimization","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université du Québec à Montréal; Transport Canada","funders":"","keywords":"Computer science; Ranking (information retrieval); Task (project management); Scalability; Preference; Rank (graph theory); Quality (philosophy); Discrete choice; Tree (set theory); Consumer choice; Product (mathematics); Transaction data; Scale (ratio); Data mining; Database transaction; Machine learning; Mathematics; Economics; Microeconomics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003694697,0.001316501,0.002175095,0.0009607368,0.0005685897,0.001508119,0.002245435,0.001745549,0.005596526],"category_scores_gemma":[0.008193608,0.00105076,0.002116367,0.001540436,0.001082143,0.001682295,0.001368264,0.002417452,0.0008962811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001379079,"about_ca_system_score_gemma":0.001848837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01340984,"about_ca_topic_score_gemma":0.01392058,"domain_scores_codex":[0.9979903,0.001153599,0.00009533646,0.000341261,0.0002375219,0.000182176],"domain_scores_gemma":[0.9935249,0.00516215,0.0003913094,0.0002427916,0.0004950447,0.0001837294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005981398,0.00004290432,0.0006329169,0.00004833984,0.0000465618,0.000049054,0.00002938036,0.97991,0.000189923,0.01016956,0.0005864742,0.008235026],"study_design_scores_gemma":[0.000004904229,0.000009818234,0.00006407887,0.00000295626,0.000004226609,0.000003590629,0.000003164383,0.9961221,0.00003075225,0.003644351,0.000106363,0.000003662517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02295652,0.0002966426,0.974265,0.0003384166,0.00004188551,0.0001079178,0.0005301679,0.000285488,0.001177946],"genre_scores_gemma":[0.6298508,0.0005277061,0.3597867,0.0004467891,0.0001078603,0.0007871546,0.001956176,0.0001541669,0.00638259],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01340984,"threshold_uncertainty_score":0.0266636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05708838260833872,"score_gpt":0.2791606620650704,"score_spread":0.2220722794567317,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}