{"id":"W603033740","doi":"","title":"Evaluation and Assessment of Recommenders Using Monte Carlo Simulation","year":2012,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Monte Carlo method; Computer science; Recommender system; Perspective (graphical); Data mining; Machine learning; Artificial intelligence; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001809334,0.0001818666,0.0002411503,0.0003840599,0.000147443,0.0001405951,0.0004497912,0.0001239603,0.00000403789],"category_scores_gemma":[0.0000228076,0.0001991534,0.00006730804,0.0003396892,0.00003432814,0.003586158,0.000357535,0.0002171774,0.000001201544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003026229,"about_ca_system_score_gemma":0.00009435746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001252669,"about_ca_topic_score_gemma":0.00006003741,"domain_scores_codex":[0.9979471,0.0005727155,0.0002606992,0.0003187411,0.0006047084,0.0002960478],"domain_scores_gemma":[0.9987119,0.0001023143,0.0002918121,0.0004660838,0.0002733027,0.000154603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003851833,0.00005809663,0.9873978,0.00002893782,0.00004082593,0.000002577075,0.0005005959,0.0007991277,0.0002942489,0.0002801373,2.064245e-7,0.01059363],"study_design_scores_gemma":[0.0006834546,0.0000852547,0.9886682,0.0001436043,0.0001014976,0.00001638993,0.0002146523,0.008185063,0.0008792438,0.0001643066,0.0005516735,0.0003066826],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8483694,0.0001262519,0.1505168,0.00007373858,0.0002446256,0.0003207502,0.000002428977,0.00009607473,0.0002499475],"genre_scores_gemma":[0.9952042,0.00002245199,0.004600966,0.00005201355,0.00003971889,0.00000113446,0.000001248379,0.00001320755,0.00006503204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1468349,"threshold_uncertainty_score":0.8121241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2786553788226275,"score_gpt":0.4173383417429903,"score_spread":0.1386829629203628,"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."}}