{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002515734,0.0001393978,0.0002177625,0.00007614134,0.0001734923,0.0004875478,0.0003075727,0.00005469091,0.000005944899],"category_scores_gemma":[0.000007480111,0.0001161944,0.00003153816,0.000140589,0.00001042313,0.0006492127,0.0003070566,0.00005522244,0.000008688524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004856942,"about_ca_system_score_gemma":0.00001428014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001965326,"about_ca_topic_score_gemma":0.00001672603,"domain_scores_codex":[0.9990466,0.00004128592,0.0002652636,0.0003169653,0.00007523395,0.0002546738],"domain_scores_gemma":[0.9992712,0.0001341138,0.00009127936,0.0002998554,0.0001188606,0.00008472616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001127504,0.00007115807,0.00147526,0.001027259,0.0002044749,0.000006633047,0.006135235,0.0000216042,0.01947637,0.6422229,0.07477257,0.2545753],"study_design_scores_gemma":[0.002074213,0.0007952317,0.001711248,0.000597433,0.00001525825,0.0002555026,0.006105396,0.9152391,0.02480329,0.008576888,0.0384848,0.001341645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002252062,0.00005980619,0.9839937,0.001028336,0.000289275,0.0008116881,0.000001565784,0.000811911,0.01075167],"genre_scores_gemma":[0.7577451,0.000005452986,0.2413601,0.0002456405,0.00003465709,0.0004338782,6.055391e-7,0.00001569956,0.0001588505],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9152175,"threshold_uncertainty_score":0.4738269,"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."}}