{"id":"W2290662141","doi":"10.32920/ryerson.14664513","title":"Applying supervised learning algorithms on information derived from Social Network to enhance recommender systems","year":2021,"lang":"en","type":"article","venue":"","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Support vector machine; Computer science; Recommender system; Pairwise comparison; Machine learning; Artificial intelligence; Rank (graph theory); Feature (linguistics); Social network (sociolinguistics); Learning to rank; Social network analysis; Data mining; Social media; World Wide Web; Ranking (information retrieval); Mathematics","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":[0.0003206932,0.0001463237,0.0001907004,0.00006154106,0.0002911461,0.0006067438,0.0005926966,0.00008349749,0.00003221164],"category_scores_gemma":[0.00008161222,0.0001504823,0.00004012388,0.0004335768,0.000007965101,0.0009732654,0.0005628481,0.0002059149,0.0001736915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008335833,"about_ca_system_score_gemma":0.00005482173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001858768,"about_ca_topic_score_gemma":0.00001043443,"domain_scores_codex":[0.998607,0.000147643,0.0002974587,0.0003210327,0.0002852717,0.0003415854],"domain_scores_gemma":[0.9991438,0.0001617955,0.00009237591,0.0004047207,0.0001203757,0.00007688146],"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":[0.000009926092,0.00003491484,0.0001963445,0.00001600053,0.00004217575,0.000009514522,0.002968906,0.001503932,0.001618878,0.007388345,0.05838131,0.9278297],"study_design_scores_gemma":[0.0006940742,0.0003532502,0.002242571,0.0004755227,0.00002202532,0.000013469,0.002711331,0.5337998,0.09554063,0.00223296,0.3603484,0.001565985],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004928017,0.00001633304,0.9875525,0.000904078,0.0004467044,0.0002811738,0.000007674698,0.0008294749,0.005034046],"genre_scores_gemma":[0.2485332,0.0000133917,0.74639,0.003810252,0.0004064859,0.0003744456,0.0002469427,0.00001653425,0.0002087525],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9262637,"threshold_uncertainty_score":0.613649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02381917398275826,"score_gpt":0.2780660719887925,"score_spread":0.2542468980060343,"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."}}