{"id":"W3159698998","doi":"10.2139/ssrn.3649342","title":"A Unified Framework for Personalizing Product Rankings","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Product (mathematics); Computer science; Business; Data science; 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":[0.009003199,0.001023914,0.001422024,0.00503641,0.002357783,0.009171684,0.003184361,0.002304444,0.01101733],"category_scores_gemma":[0.02959539,0.0009040492,0.001814781,0.005645002,0.002271565,0.01081389,0.003728516,0.002923729,0.003593208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00221423,"about_ca_system_score_gemma":0.003909218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01058416,"about_ca_topic_score_gemma":0.01640558,"domain_scores_codex":[0.9905358,0.003566302,0.000653829,0.001674858,0.002922101,0.0006470898],"domain_scores_gemma":[0.98659,0.004822415,0.0007651797,0.00369879,0.003543022,0.0005805141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007820444,0.0002115113,0.003322082,0.0001367583,0.0001255575,0.0001429158,0.001286043,0.02575117,0.001124072,0.7636597,0.01192808,0.1922339],"study_design_scores_gemma":[0.00003737306,0.00009585667,0.001554569,0.0001184566,0.00009506428,0.0001870108,0.000597402,0.2475613,0.001441434,0.7037721,0.04443476,0.0001045996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003534749,0.0002206824,0.9820389,0.0009989586,0.00008653462,0.0001857538,0.0003237023,0.0009424977,0.01166821],"genre_scores_gemma":[0.1618033,0.0004921384,0.8267571,0.000305973,0.0002125578,0.0003713018,0.0007320488,0.0002310865,0.009094452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01101733,"threshold_uncertainty_score":0.04761398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02698586674603959,"score_gpt":0.2522332030583111,"score_spread":0.2252473363122715,"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."}}