{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001976816,0.001258173,0.001215233,0.004835333,0.0008342488,0.001305913,0.0009561499,0.001302323,0.001267033],"category_scores_gemma":[0.009530265,0.0004239795,0.001491441,0.003343001,0.0002180935,0.001942845,0.0006500454,0.0008654617,0.001282384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008633232,"about_ca_system_score_gemma":0.0006447002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02111092,"about_ca_topic_score_gemma":0.04295901,"domain_scores_codex":[0.9977571,0.0005010135,0.000277623,0.0006579044,0.0007016051,0.0001047288],"domain_scores_gemma":[0.9945716,0.002821073,0.0005340085,0.0005467275,0.0013784,0.0001482105],"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":[0.001191732,0.001572335,0.2508599,0.001932861,0.00287605,0.001076494,0.0009563523,0.07083282,0.02802328,0.003810701,0.02284598,0.6140215],"study_design_scores_gemma":[0.0001145184,0.000835927,0.1318677,0.0002193638,0.0008427118,0.0008982463,0.0006698841,0.8289873,0.01431567,0.003002445,0.0180228,0.0002234162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6321225,0.007991229,0.3174048,0.002429697,0.000874711,0.001692327,0.02032877,0.003674271,0.01348159],"genre_scores_gemma":[0.8174559,0.001835747,0.1590455,0.0002866606,0.0002444201,0.0004625424,0.01372928,0.00006060597,0.006879415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02111092,"threshold_uncertainty_score":0.04197609,"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."}}