{"id":"W2124085800","doi":"10.1145/1363686.1363903","title":"Imputation-boosted collaborative filtering using machine learning classifiers","year":2008,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Imputation (statistics); Computer science; Artificial intelligence; Collaborative filtering; Machine learning; Naive Bayes classifier; Classifier (UML); Missing data; Pattern recognition (psychology); Support vector machine; Data mining; Recommender system","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":[0.01361063,0.001371921,0.004866087,0.002315346,0.001466974,0.002149175,0.004505325,0.00317606,0.001995366],"category_scores_gemma":[0.04505942,0.001058495,0.002170642,0.003405846,0.001125031,0.003798448,0.00178453,0.00365287,0.001621341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001077815,"about_ca_system_score_gemma":0.001553188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007553147,"about_ca_topic_score_gemma":0.006396116,"domain_scores_codex":[0.990866,0.00422588,0.0004744846,0.002026642,0.001962314,0.0004447283],"domain_scores_gemma":[0.9648071,0.02295898,0.00169369,0.005722783,0.004384936,0.0004325024],"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":[0.000485417,0.0004995536,0.007402375,0.0003180966,0.000538496,0.0002667031,0.0003591359,0.4609416,0.002964985,0.02232475,0.008359241,0.4955397],"study_design_scores_gemma":[0.0000246149,0.00006434057,0.0003910767,0.00001477687,0.00004051992,0.00007926557,0.00001484571,0.9857596,0.001022336,0.0115898,0.0009793859,0.00001941682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004699052,0.0002050732,0.993935,0.0001517292,0.00004635964,0.0000409037,0.00005843187,0.000417815,0.0004455684],"genre_scores_gemma":[0.3445108,0.0004277408,0.6503776,0.0004376381,0.0004185409,0.0002484787,0.0007402006,0.0001015455,0.002737523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01361063,"threshold_uncertainty_score":0.07198077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04486225412459702,"score_gpt":0.2716267722170551,"score_spread":0.2267645180924581,"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."}}