{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002027091,0.001099451,0.001949075,0.001876838,0.0008170382,0.001399999,0.002362189,0.001976913,0.003446874],"category_scores_gemma":[0.004226092,0.0006335643,0.001059225,0.00215785,0.000292733,0.002151045,0.001031627,0.001096364,0.003015405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004774921,"about_ca_system_score_gemma":0.0008931124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0164003,"about_ca_topic_score_gemma":0.02623146,"domain_scores_codex":[0.9986231,0.0003096546,0.00009594591,0.0004197933,0.0004695189,0.00008195316],"domain_scores_gemma":[0.9977788,0.0007257717,0.00009258727,0.0003468486,0.0009686979,0.00008737858],"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":[0.0005973696,0.0004622797,0.008706531,0.0002791204,0.0007147508,0.0004805746,0.0002643518,0.139664,0.01539935,0.005448493,0.02039813,0.8075851],"study_design_scores_gemma":[0.00006118234,0.0001321565,0.001556616,0.00001784416,0.0001956418,0.0002543067,0.00002404569,0.9878301,0.002422444,0.001773537,0.005667196,0.00006486983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03962199,0.001382317,0.947881,0.0006224998,0.0004035455,0.0001678144,0.0007921181,0.004407747,0.004721033],"genre_scores_gemma":[0.3660981,0.0009141905,0.6145935,0.0006176064,0.0004299237,0.0002743365,0.00175949,0.0001745382,0.01513838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0164003,"threshold_uncertainty_score":0.0326097,"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."}}