{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003097617,0.0008428604,0.001285648,0.002332345,0.000521828,0.00117538,0.001013035,0.0009743553,0.001318068],"category_scores_gemma":[0.01144764,0.0003485855,0.0006843122,0.00166909,0.0003257484,0.001675291,0.0007692563,0.001017007,0.0008415875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005760341,"about_ca_system_score_gemma":0.0006638621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003316115,"about_ca_topic_score_gemma":0.005276839,"domain_scores_codex":[0.9984037,0.0006845263,0.000125953,0.0002878767,0.0004205541,0.00007735177],"domain_scores_gemma":[0.993161,0.00436463,0.0004309866,0.0005485139,0.001402529,0.0000924944],"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":[0.0002740001,0.0007646098,0.008367932,0.000320531,0.0003814534,0.0001128382,0.0002444994,0.248597,0.004431327,0.006369701,0.005107966,0.7250281],"study_design_scores_gemma":[0.00001549902,0.00007739989,0.0009269581,0.00001730277,0.00002490464,0.00002693481,0.00003571125,0.9927233,0.001159501,0.003985275,0.0009973117,0.00000989928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04504723,0.0006881513,0.9497522,0.0003689607,0.0001436923,0.0002307122,0.0002187687,0.0009636045,0.002586759],"genre_scores_gemma":[0.5280569,0.0005460759,0.4672006,0.0002042519,0.0002499708,0.000315878,0.0007604271,0.00008021499,0.002585583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003316115,"threshold_uncertainty_score":0.01638198,"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."}}