{"id":"W3085631657","doi":"10.1287/opre.2022.2380","title":"Learning Product Rankings Robust to Fake Users","year":2022,"lang":"en","type":"article","venue":"Operations Research","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Leverage (statistics); Computer science; Product (mathematics); Ranking (information retrieval); Analytics; Status quo; Data science; Learning to rank; Machine learning; Artificial intelligence; Mathematics; Economics","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":["metaresearch","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.01857942,0.0001389379,0.0002349536,0.001880242,0.00543089,0.001225635,0.002117947,0.00003152977,0.009889035],"category_scores_gemma":[0.02046392,0.000120417,0.00007880164,0.007952555,0.0002196839,0.0006137092,0.002158811,0.001851909,0.002753586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004677001,"about_ca_system_score_gemma":0.0006478116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003143187,"about_ca_topic_score_gemma":0.0003622347,"domain_scores_codex":[0.9869181,0.002537455,0.0005360714,0.001108189,0.00794044,0.0009597289],"domain_scores_gemma":[0.9948968,0.001473265,0.00002580398,0.001107328,0.002167231,0.0003295749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004994724,0.00008097888,0.0007380071,0.000001442856,0.000007895924,0.0000272075,0.001991186,0.9304064,0.002773051,0.0004353774,0.02266098,0.04082753],"study_design_scores_gemma":[0.0006176202,0.0008240007,0.00263566,0.000007201054,0.00000220739,0.00004118141,0.01523999,0.1754518,0.002348266,0.001181809,0.8012902,0.0003600085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8243497,0.0003916318,0.09981471,0.05023595,0.001050472,0.004988674,0.0001214879,0.0002947776,0.0187526],"genre_scores_gemma":[0.8474913,0.00001338706,0.00847573,0.0001352244,0.0002714891,0.0009768524,0.00002644528,0.00004067156,0.142569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7786292,"threshold_uncertainty_score":0.9998112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3625629463949137,"score_gpt":0.5291875609008896,"score_spread":0.1666246145059759,"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."}}