{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000268994,0.0001217307,0.00009870865,0.0003109948,0.0009603342,0.0002288246,0.00009100521,0.000008723023,0.0003139603],"category_scores_gemma":[0.00001168185,0.0001150503,0.00002062432,0.0004569605,0.00002786102,0.000427495,0.0002263908,0.0001197484,0.000007788377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004753484,"about_ca_system_score_gemma":0.00000918239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001983849,"about_ca_topic_score_gemma":0.001586978,"domain_scores_codex":[0.9991402,0.00001582535,0.0001665802,0.000327023,0.0002009011,0.0001494797],"domain_scores_gemma":[0.9996771,0.000005328871,0.00004958149,0.0001998911,0.0000592218,0.00000886043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004676314,0.001271336,0.04303201,0.0009453768,0.0002132886,0.00006976643,0.0005922396,0.2876894,0.001201498,0.01139532,0.002682405,0.6504397],"study_design_scores_gemma":[0.002250425,0.00009088878,0.09293897,0.0001409974,0.0005847467,0.00003947006,0.01108882,0.5991576,0.00004277103,0.00009569917,0.2925388,0.001030875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.946799,0.0003075852,0.0267404,0.01478347,0.001309527,0.001850024,0.000007885908,0.0004637966,0.007738333],"genre_scores_gemma":[0.9947782,0.00001230207,0.001351882,0.001474487,0.0001125008,0.0001569576,0.00007691934,0.00001945008,0.002017367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6494089,"threshold_uncertainty_score":0.7386211,"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."}}