{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004848992,0.0002107378,0.0002364279,0.0001810153,0.0004068439,0.0007419483,0.0004262623,0.00009038261,0.0002931104],"category_scores_gemma":[0.0003232024,0.0001826423,0.00009834271,0.000338917,0.00001163468,0.003505996,0.0001722646,0.0002096941,0.00002368664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004684772,"about_ca_system_score_gemma":0.00007060088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000714293,"about_ca_topic_score_gemma":0.00001667556,"domain_scores_codex":[0.9984521,0.000008927427,0.000578105,0.0002609556,0.0003776114,0.0003222542],"domain_scores_gemma":[0.9987449,0.00005122237,0.0005243117,0.0002583902,0.0003650186,0.00005613978],"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":[0.0002422241,0.00006286213,0.001558634,0.00004017348,0.00003738449,0.000001709978,0.000127087,0.9917213,0.000006499926,0.0001351934,0.003512249,0.002554707],"study_design_scores_gemma":[0.002008321,0.00003240037,0.0001812909,0.00003835131,0.0001671941,0.000002226612,0.00005197541,0.9823488,0.0000034701,0.00002588771,0.01490254,0.0002375438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001968817,0.0000163476,0.9924224,0.003195889,0.0003054491,0.0005399216,0.00003567414,0.0001171322,0.001398373],"genre_scores_gemma":[0.6658792,0.0002747014,0.2896505,0.03222621,0.006358405,0.0001413795,0.004785074,0.0002442769,0.0004402073],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7027719,"threshold_uncertainty_score":0.7447939,"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."}}