{"id":"W2065874310","doi":"10.1109/aina.2013.65","title":"Combining Collaborative Filtering and Clustering for Implicit Recommender System","year":2013,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Recommender system; Collaborative filtering; Computer science; Cluster analysis; Matrix decomposition; Personalization; Data mining; 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":[0.005792481,0.00148857,0.002798295,0.003201818,0.001476329,0.001865152,0.002831895,0.0025248,0.001202684],"category_scores_gemma":[0.01506129,0.0008309282,0.001939053,0.003143785,0.000942854,0.002940911,0.002068693,0.001837932,0.00109632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001128081,"about_ca_system_score_gemma":0.001446314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01039843,"about_ca_topic_score_gemma":0.01538102,"domain_scores_codex":[0.9939635,0.002032303,0.0004078638,0.001220589,0.002101196,0.0002745604],"domain_scores_gemma":[0.9906254,0.004453504,0.0004852523,0.001936948,0.00232632,0.0001725978],"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":[0.0003168918,0.0003712384,0.003957362,0.000564772,0.0007550244,0.0001481,0.0005912802,0.3456758,0.01439951,0.02268098,0.005340552,0.6051985],"study_design_scores_gemma":[0.00002374125,0.00008128553,0.0009222064,0.00002246534,0.00008355565,0.00008122919,0.00003887426,0.9825379,0.003849949,0.009365272,0.002936658,0.00005699262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004495851,0.0002927517,0.9940525,0.00009012808,0.00003587216,0.00005976079,0.0000385706,0.0003265165,0.0006081213],"genre_scores_gemma":[0.1635128,0.0005651953,0.8317614,0.0001583326,0.0002243602,0.0002030241,0.00039797,0.0001115423,0.003065296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01039843,"threshold_uncertainty_score":0.03063393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01918542441364978,"score_gpt":0.2529371465449354,"score_spread":0.2337517221312856,"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."}}