{"id":"W3207790912","doi":"10.1287/mnsc.2021.4130","title":"Learning to Rank an Assortment of Products","year":2021,"lang":"en","type":"article","venue":"Management Science","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Clickstream; Computer science; Ranking (information retrieval); Learning to rank; Product (mathematics); Analytics; Rank (graph theory); Set (abstract data type); Exploit; Machine learning; Data science; Marketing; Business; World Wide Web; The Internet; Mathematics","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":[0.001186737,0.0001105319,0.0001311186,0.0003795908,0.0002717133,0.0003110358,0.0004236519,0.00001212569,0.0002013345],"category_scores_gemma":[0.0001549469,0.000110135,0.00002886716,0.002434227,0.00008042915,0.001004844,0.0006717409,0.00006844549,0.0001071594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002724716,"about_ca_system_score_gemma":0.00002467667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006704179,"about_ca_topic_score_gemma":0.00003452303,"domain_scores_codex":[0.9983888,0.00001196268,0.0002034704,0.000500104,0.0005768997,0.0003187343],"domain_scores_gemma":[0.9992169,0.00001028054,0.00009568191,0.0004065585,0.0002479228,0.0000226795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005572326,0.0005273721,0.2645023,0.0006452031,0.00003774398,0.0001602139,0.0005172459,0.001354461,0.05993441,0.05563065,0.001433655,0.6152009],"study_design_scores_gemma":[0.00050951,0.00003398887,0.7792387,0.0001081082,0.0001342467,0.000002330657,0.001905364,0.001067733,0.007477477,0.0004028929,0.2086708,0.0004488199],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9107088,0.00002437371,0.0002732438,0.0008893218,0.0004336262,0.0002816347,2.400108e-7,0.00008648154,0.08730227],"genre_scores_gemma":[0.995548,0.000005494236,0.001595829,0.0006122302,0.00009715976,0.00001567909,0.000005073904,0.000009765868,0.002110735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6147522,"threshold_uncertainty_score":0.4491174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02218394665383126,"score_gpt":0.2574121551592731,"score_spread":0.2352282085054419,"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."}}