{"id":"W2810374059","doi":"10.1109/tg.2018.2844121","title":"Recommender System for Items in <i>Dota 2</i>","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Games","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Recommender system; Purchasing; Computer science; HERO; Cluster analysis; Logistic regression; Affect (linguistics); Feature (linguistics); Artificial intelligence; Psychology; Machine learning; Marketing; Business; Communication","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":[0.0002936361,0.0001519201,0.0001759983,0.0002057734,0.0001626852,0.0001104185,0.0005973013,0.00008623885,0.00003237085],"category_scores_gemma":[0.0000084264,0.0001466414,0.000101319,0.0004266923,0.00009156334,0.0003574893,0.000002522175,0.0001432323,0.0002367093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001215722,"about_ca_system_score_gemma":0.00004735899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008569637,"about_ca_topic_score_gemma":0.0002623472,"domain_scores_codex":[0.9986875,0.00006036448,0.0003325807,0.0004143654,0.0001694041,0.0003358003],"domain_scores_gemma":[0.9989486,0.000284302,0.00006221082,0.0005231121,0.0001091959,0.00007252563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001652185,0.000697204,0.0001188345,0.000135189,0.00007738124,0.00001557046,0.006642885,0.005503157,0.006502318,0.0192007,0.009379052,0.9515625],"study_design_scores_gemma":[0.0004438872,0.0006981841,0.00005774058,0.000187818,0.00001790599,0.00003242681,0.001042834,0.2610625,0.6957256,0.003065177,0.0370822,0.0005837548],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004965094,0.00002720665,0.9888145,0.0009023299,0.002604454,0.0003433643,0.000009832036,0.0002745057,0.002058745],"genre_scores_gemma":[0.9843254,0.000009265036,0.01426549,0.0003839722,0.0001271108,0.0001624966,3.006017e-7,0.00001676949,0.0007091672],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9793603,"threshold_uncertainty_score":0.5979863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04347225596915313,"score_gpt":0.2961998781675012,"score_spread":0.252727622198348,"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."}}