{"id":"W4285464558","doi":"10.32920/ryerson.14664513.v1","title":"Applying supervised learning algorithms on information derived from Social Network to enhance recommender systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Support vector machine; Computer science; Recommender system; Pairwise comparison; Machine learning; Rank (graph theory); Feature (linguistics); Artificial intelligence; 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":[0.002920393,0.0008660401,0.001197926,0.002128088,0.0004680079,0.001151875,0.0009471108,0.001011088,0.001449607],"category_scores_gemma":[0.01087089,0.0003444653,0.0006852637,0.001513569,0.0003187778,0.001583676,0.0007440239,0.0009642021,0.000972947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005387975,"about_ca_system_score_gemma":0.0005605721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002710625,"about_ca_topic_score_gemma":0.004219361,"domain_scores_codex":[0.9984647,0.0006724988,0.0001142766,0.0002783664,0.0003975951,0.00007259641],"domain_scores_gemma":[0.9939266,0.003794148,0.0003879044,0.0004990805,0.001310108,0.00008222779],"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":[0.0002868374,0.0006556613,0.007110047,0.0003620809,0.0004092382,0.0001139904,0.0002408531,0.2300779,0.006093958,0.006630264,0.006025668,0.7419934],"study_design_scores_gemma":[0.00001659047,0.00007262832,0.0008658443,0.00001745156,0.00002692063,0.00002552367,0.00003330607,0.9922545,0.00128942,0.004319419,0.001068867,0.000009379144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04053294,0.0006729371,0.9542737,0.0003490096,0.0001422732,0.0002018895,0.0002120458,0.0009222804,0.002692827],"genre_scores_gemma":[0.526006,0.0005791619,0.468874,0.0002207655,0.0002915105,0.0003195355,0.0008320178,0.00009735816,0.002779606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002920393,"threshold_uncertainty_score":0.0154447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03167334953480581,"score_gpt":0.2871892697441136,"score_spread":0.2555159202093078,"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."}}