{"id":"W2734595044","doi":"10.1145/3099023.3099114","title":"Recommender Systems for Personalized Gamification","year":2017,"lang":"en","type":"article","venue":"","topic":"Educational Games and Gamification","field":"Psychology","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Recommender system; Variety (cybernetics); Context (archaeology); Human–computer interaction; Data science; World Wide Web; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002662844,0.00006297076,0.00008031383,0.00002800928,0.000263401,0.0001143545,0.0002105555,0.00006139085,0.0008711544],"category_scores_gemma":[0.00006690469,0.00005584292,0.00004879837,0.00001344019,0.00004351295,0.00008155678,0.000008125424,0.00003457736,0.0003092489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002627797,"about_ca_system_score_gemma":0.00001680697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001853471,"about_ca_topic_score_gemma":0.000004947791,"domain_scores_codex":[0.9994383,0.00002729274,0.0001428977,0.0002032507,0.00006242334,0.0001257843],"domain_scores_gemma":[0.9990289,0.00008585677,0.0001645135,0.0005646607,0.0001162247,0.00003989239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002852392,0.00008326718,0.002120872,0.00001141753,0.0000332564,4.422899e-8,0.0009249602,8.063261e-7,0.0001158994,0.8593335,0.1294798,0.007867673],"study_design_scores_gemma":[0.0005302818,0.00002708765,0.1120845,0.000006159049,0.00001483029,0.000002729736,0.002490912,0.0002665343,0.00001879499,0.001476565,0.8829819,0.00009963644],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.05406049,0.001907201,0.1562332,0.0513157,0.0114578,0.002432109,0.00007973479,0.0002352122,0.7222785],"genre_scores_gemma":[0.7710294,0.00002682008,0.0008275069,0.0002607154,0.0003787063,0.0005472391,0.00007126069,0.00001313816,0.2268453],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8578569,"threshold_uncertainty_score":0.953853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1211049985890719,"score_gpt":0.4130554455560144,"score_spread":0.2919504469669424,"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."}}