{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002664055,0.001198437,0.001720502,0.001298001,0.0006305475,0.00165208,0.002017083,0.001448202,0.002715149],"category_scores_gemma":[0.01421107,0.0004915374,0.0005725717,0.00159908,0.0009161797,0.003671051,0.001063166,0.001728663,0.0006711198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00113938,"about_ca_system_score_gemma":0.0007845691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003338553,"about_ca_topic_score_gemma":0.003906224,"domain_scores_codex":[0.9987788,0.0003679206,0.00006183668,0.0004470507,0.0002093527,0.0001352051],"domain_scores_gemma":[0.9915555,0.005920608,0.0009089288,0.0007613906,0.0004565097,0.0003970761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007825702,0.001237954,0.0306393,0.0001551898,0.0001650579,0.0002012228,0.0001754087,0.6770864,0.003262011,0.01792598,0.004095668,0.2642732],"study_design_scores_gemma":[0.00002237515,0.0001075979,0.001048363,0.000004600096,0.00001043725,0.000033303,0.00002344336,0.9872946,0.000630162,0.01049272,0.0003237285,0.000008719971],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4991528,0.0005784188,0.4951369,0.001048291,0.00005968279,0.0002181158,0.0004446393,0.0005311295,0.002829946],"genre_scores_gemma":[0.8898342,0.0002082553,0.1053663,0.0001825711,0.00009043892,0.00009560282,0.0006757152,0.00004550904,0.003501433],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003338553,"threshold_uncertainty_score":0.01408905,"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."}}