{"id":"W1995221706","doi":"10.5539/cis.v6n4p88","title":"Data Mining Techniques and Preference Learning in Recommender Systems","year":2013,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Recommender system; Computer science; Component (thermodynamics); Preference; Set (abstract data type); Association rule learning; Order (exchange); Preference learning; Information retrieval; World Wide Web; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.007564726,0.001209923,0.002025638,0.003614507,0.0007907987,0.002458035,0.002019065,0.001798146,0.002131858],"category_scores_gemma":[0.02686565,0.0008364979,0.001632823,0.005683406,0.00111061,0.003355392,0.001258398,0.002856868,0.001254405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001150143,"about_ca_system_score_gemma":0.001183343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005357268,"about_ca_topic_score_gemma":0.004785957,"domain_scores_codex":[0.9927477,0.003750753,0.0007472809,0.0008482775,0.001746196,0.0001598976],"domain_scores_gemma":[0.9784828,0.01668125,0.0008734562,0.001544876,0.00221844,0.0001992533],"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":[0.0003095224,0.0006849683,0.01310227,0.002237524,0.0009192816,0.0003678394,0.000576398,0.150489,0.001992155,0.08545921,0.008342159,0.7355197],"study_design_scores_gemma":[0.0001347205,0.0002512142,0.003554235,0.000352161,0.0001926554,0.0005032041,0.0002857266,0.7732968,0.00287012,0.1997084,0.01872817,0.000122471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007005297,0.006597623,0.9819769,0.001372996,0.000159943,0.0002063611,0.0004540134,0.0003424798,0.001884504],"genre_scores_gemma":[0.1945328,0.009202756,0.7909691,0.0005817072,0.0005548752,0.000609046,0.001187203,0.00005895735,0.002303594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007564726,"threshold_uncertainty_score":0.04000658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06092787821162245,"score_gpt":0.2794478995151464,"score_spread":0.2185200213035239,"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."}}