{"id":"W2585098625","doi":"10.1145/3027141.3027144","title":"ACM UMAP 2017 - User Modeling, Adaptation and Personalization","year":2017,"lang":"en","type":"article","venue":"ACM SIGWEB Newsletter","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Adaptive hypermedia; Computer science; Personalization; User modeling; Adaptation (eye); World Wide Web; Hypermedia; Successor cardinal; User profile; Multimedia; User interface","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":[0.0002526347,0.0001432174,0.0001472785,0.00006719329,0.0004368885,0.0008035444,0.001795491,0.00009265157,0.00001356296],"category_scores_gemma":[0.0001897593,0.0001264725,0.00004535731,0.00003226632,0.00003276666,0.001374504,0.0008337069,0.0001063831,0.00003257039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000208858,"about_ca_system_score_gemma":0.00002075252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004167036,"about_ca_topic_score_gemma":0.00005115054,"domain_scores_codex":[0.9989666,0.00004850737,0.0001909366,0.0003878643,0.0002025542,0.0002035228],"domain_scores_gemma":[0.9974464,0.00004220885,0.0001639161,0.002211075,0.00007095999,0.00006549122],"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":[0.00002371641,0.0001351352,0.03829401,0.0001894007,0.0001516268,0.00009071538,0.01220728,0.0002331404,0.007176816,0.05197017,0.7082465,0.1812814],"study_design_scores_gemma":[0.0009753695,0.0001330927,0.01104814,0.0001536914,0.00002637948,0.0000758124,0.00009831318,0.8068916,0.001264091,0.03986304,0.1386436,0.0008268549],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03414185,0.0001275507,0.9297359,0.03351702,0.0004333078,0.0002255266,0.000001444207,0.0001978759,0.001619521],"genre_scores_gemma":[0.9177762,0.000044242,0.07801796,0.003058608,0.0002408789,0.00003043064,0.000004233984,0.00001491065,0.0008125645],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8836343,"threshold_uncertainty_score":0.7748597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1008983977490054,"score_gpt":0.3038669387092631,"score_spread":0.2029685409602577,"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."}}