{"id":"W4206643812","doi":"10.1007/s11257-021-09304-9","title":"“Knowing me, knowing you”: personalized explanations for a music recommender system","year":2022,"lang":"en","type":"article","venue":"User Modeling and User-Adapted Interaction","topic":"Neuroscience and Music Perception","field":"Neuroscience","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Onderzoeksraad, KU Leuven","keywords":"Sophistication; Recommender system; Preference; Personalization; Openness to experience; Computer science; Need for cognition; Perception; Cognition; Domain (mathematical analysis); Musical; Human–computer interaction; Psychology; Cognitive psychology; World Wide Web; Social psychology; Aesthetics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003910088,0.0001961404,0.0001998978,0.0003166026,0.00155216,0.0001728708,0.0001862847,0.0000488165,0.0001092977],"category_scores_gemma":[0.0001600049,0.0002063187,0.0001175323,0.0003236663,0.00002595973,0.0008890323,0.0001157535,0.0003372438,0.000008334589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002795677,"about_ca_system_score_gemma":0.00004151401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001094571,"about_ca_topic_score_gemma":0.00001870033,"domain_scores_codex":[0.9980891,0.0001912106,0.0003554094,0.0006788468,0.0003487404,0.0003367026],"domain_scores_gemma":[0.9993142,0.0001782812,0.0001304119,0.000217387,0.00006881548,0.0000908435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004025981,0.0002201175,0.00001932533,0.00009190864,0.00001792744,0.00001117504,0.007484899,0.07671867,0.8967224,0.00573058,0.003663694,0.008916711],"study_design_scores_gemma":[0.0007653068,0.0001390495,0.000008974951,0.00007027182,0.0000409803,0.0001211536,0.01195191,0.9219418,0.001810225,0.00005899102,0.06283886,0.000252516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8730566,0.00002213788,0.1219677,0.0006454937,0.002476959,0.0005844975,0.00004980691,0.0003247645,0.0008720896],"genre_scores_gemma":[0.9960583,0.00003145765,0.0007009167,0.00131983,0.0001683632,0.0003492074,0.00002762076,0.00003755556,0.001306785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8949122,"threshold_uncertainty_score":0.9997477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1229134333904537,"score_gpt":0.3171876232103994,"score_spread":0.1942741898199457,"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."}}