{"id":"W4232058686","doi":"10.1109/wi.2007.4427165","title":"Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts","year":2007,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Collaborative filtering; Computer science; Recommender system; Algorithm; Sparse matrix; Machine learning; Mixture model; Data mining; Artificial intelligence","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":[0.0003155248,0.00008956446,0.0001519188,0.00009375408,0.0000455199,0.00004933025,0.0003220221,0.00003210344,0.00001000844],"category_scores_gemma":[0.000007176461,0.00007197711,0.00003625691,0.0002396248,0.00001589988,0.0002827517,0.000141387,0.00003851334,7.342892e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003005387,"about_ca_system_score_gemma":0.00003029668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001228094,"about_ca_topic_score_gemma":0.000009231959,"domain_scores_codex":[0.9992338,0.00002399288,0.0002326238,0.0001891838,0.0001472877,0.0001730603],"domain_scores_gemma":[0.9994135,0.00004147822,0.00009318545,0.0002895543,0.000112901,0.00004934624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002313111,0.00033542,0.001467039,0.0001428548,0.0001647297,0.0003577771,0.01291764,0.00003622194,0.4206369,0.09615353,0.02528813,0.4424766],"study_design_scores_gemma":[0.0001445349,0.00008145117,0.0001101882,0.00005740594,0.000001693722,0.00007641358,0.0002507969,0.01821653,0.9699382,0.001053613,0.009888665,0.0001804999],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0193589,0.0001283088,0.9732115,0.00007356523,0.0002375926,0.0001278535,0.000001375157,0.0001398189,0.006721118],"genre_scores_gemma":[0.5499693,0.000003534511,0.4498162,0.00007824549,0.00003774174,0.000002371632,2.87411e-7,0.000004226494,0.00008814629],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5493013,"threshold_uncertainty_score":0.2935141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02490052246490327,"score_gpt":0.2927450723134844,"score_spread":0.2678445498485811,"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."}}