{"id":"W2912615039","doi":"10.1111/poms.13783","title":"Context‐based dynamic pricing with online clustering","year":2022,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Dynamic pricing; Revenue management; Cluster analysis; Context (archaeology); Computer science; Regret; Product (mathematics); Benchmark (surveying); Pricing strategies; Set (abstract data type); Revenue; Data set; Business; Marketing; Machine learning; Artificial intelligence","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":[0.002076624,0.001398572,0.002458027,0.001088614,0.0009452493,0.001730437,0.003338071,0.002126576,0.002239023],"category_scores_gemma":[0.007442594,0.0007774074,0.0009767914,0.001992084,0.001003977,0.002759401,0.001584241,0.0019715,0.0003201331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001672568,"about_ca_system_score_gemma":0.001766346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01343537,"about_ca_topic_score_gemma":0.009205999,"domain_scores_codex":[0.9982553,0.0005883275,0.00007935675,0.000492027,0.0002756862,0.0003092514],"domain_scores_gemma":[0.996547,0.002094901,0.0003592936,0.000304245,0.0003731977,0.0003214084],"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":[0.0001075526,0.0001739521,0.001446594,0.00003655712,0.00005376558,0.00007984512,0.00004593388,0.9720643,0.0003837229,0.006418136,0.0009554895,0.01823413],"study_design_scores_gemma":[0.000006095767,0.00001188251,0.0001234498,0.000001500832,0.000004449592,0.00001198597,0.000008380179,0.997099,0.00007428879,0.002540533,0.000113596,0.000004902891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2100511,0.001518449,0.7792959,0.001083375,0.0003075348,0.0002694568,0.0003421138,0.0009648643,0.006167033],"genre_scores_gemma":[0.938444,0.0002392911,0.05942557,0.0001909386,0.0001021366,0.00007922274,0.0002139265,0.00005796103,0.001247029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01343537,"threshold_uncertainty_score":0.02671432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01567807914918206,"score_gpt":0.2329767371427124,"score_spread":0.2172986579935304,"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."}}