{"id":"W2146665816","doi":"10.1109/waina.2011.12","title":"An Improved Hybrid Recommender System by Combining Predictions","year":2011,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Recommender system; Collaborative filtering; Computer science; Cold start (automotive); The Internet; Artificial intelligence; Information retrieval; Machine learning; World Wide Web","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":[0.0003823111,0.0001485199,0.0001763267,0.00008326236,0.000180319,0.0001520393,0.0008515911,0.00005415413,0.00003814249],"category_scores_gemma":[0.000002832279,0.000125599,0.00005532602,0.0001379856,0.00001550615,0.0008381669,0.0001362201,0.0001219936,0.00002210799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005810722,"about_ca_system_score_gemma":0.00002366105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007407037,"about_ca_topic_score_gemma":0.00001084407,"domain_scores_codex":[0.9987878,0.0001065521,0.0003163431,0.0003963005,0.0001186046,0.0002744567],"domain_scores_gemma":[0.9988245,0.00002221296,0.0001033945,0.0008278308,0.00006238933,0.0001596974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001330195,0.001001087,0.003444536,0.0001262073,0.0002016875,0.00003046551,0.004067346,0.00000107117,0.004971412,0.4303047,0.4820885,0.07374962],"study_design_scores_gemma":[0.00158646,0.002249082,0.001077118,0.0001817089,0.00003702076,0.0006295146,0.002281347,0.8260896,0.08980352,0.005647221,0.06877574,0.001641712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006374986,0.00003131996,0.9174638,0.0001453555,0.0006615737,0.0002255414,0.000005708059,0.002145503,0.07868372],"genre_scores_gemma":[0.9266143,0.000004160403,0.07266319,0.0002287617,0.00003479569,0.00008028263,0.000005870373,0.00001395305,0.0003546188],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9259769,"threshold_uncertainty_score":0.5121778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02893286477689483,"score_gpt":0.235592593050395,"score_spread":0.2066597282735002,"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."}}