{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02591882,0.003418177,0.004286724,0.004044357,0.005561551,0.01673765,0.006876366,0.009215686,0.08597847],"category_scores_gemma":[0.04147725,0.002213677,0.002808736,0.007078097,0.004784847,0.02509339,0.0151776,0.01615328,0.0458404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007275754,"about_ca_system_score_gemma":0.01572462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02453834,"about_ca_topic_score_gemma":0.01805809,"domain_scores_codex":[0.9781876,0.008083553,0.001781446,0.002720593,0.007677768,0.001549068],"domain_scores_gemma":[0.9695327,0.008736843,0.0006910086,0.008928073,0.008665289,0.003446117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001953747,0.0002053845,0.0005608593,0.0005693082,0.0000822836,0.0001971701,0.0005940606,0.002658954,0.0007647235,0.1116414,0.5809702,0.3015603],"study_design_scores_gemma":[0.0000347047,0.00005204956,0.0004132816,0.0005686243,0.00003556296,0.0002403898,0.0002030722,0.008273486,0.0005742376,0.05750651,0.9320363,0.00006176277],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.003694995,0.0550278,0.6091658,0.06327526,0.04260892,0.002317128,0.004979073,0.01337527,0.2055558],"genre_scores_gemma":[0.06627523,0.1000292,0.2551086,0.01050702,0.03400227,0.005235319,0.03618354,0.01019827,0.4824605],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.08597847,"threshold_uncertainty_score":0.2876267,"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."}}