{"id":"W2910503227","doi":"10.1109/iemcon.2018.8614793","title":"Recommender System based on Extracted Data from Different Social Media. A Study of Twitter and LinkedIn","year":2018,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Recommender system; Social media; Computer science; Confusion; World Wide Web; Data science; Information retrieval; Psychology","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":[0.0003732283,0.0001692306,0.0003290152,0.0001125812,0.0001010738,0.0001186916,0.001043467,0.00008752847,0.00004181416],"category_scores_gemma":[0.00001761395,0.0001182131,0.00002490751,0.0001200052,0.00003125833,0.0002078863,0.0005695331,0.0001212675,0.000006554601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003059386,"about_ca_system_score_gemma":0.00001634331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004256952,"about_ca_topic_score_gemma":0.0003661716,"domain_scores_codex":[0.9983424,0.0002262049,0.0003867707,0.0005537186,0.0003186474,0.0001722735],"domain_scores_gemma":[0.9982536,0.0003377575,0.0001623544,0.001108715,0.00006991426,0.00006765972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004086609,0.01203394,0.09874531,0.0004630742,0.00100974,0.0001046195,0.07186893,5.571251e-7,0.003823355,0.01531533,0.5731012,0.2231253],"study_design_scores_gemma":[0.009581564,0.004381502,0.3667151,0.0006992415,0.0001564173,0.00001356979,0.0132066,0.5845882,0.007411919,0.00146137,0.009982104,0.001802452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5967023,0.00001714613,0.3947195,0.001615642,0.001215143,0.0007862733,0.00004803025,0.0005964909,0.004299541],"genre_scores_gemma":[0.9946403,7.843954e-7,0.004743521,0.0002296939,0.0003159836,0.0000215802,0.00001850772,0.00001053445,0.00001911393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5845876,"threshold_uncertainty_score":0.4820589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1504747076086898,"score_gpt":0.3214886228645316,"score_spread":0.1710139152558418,"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."}}