{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001451731,0.0007770231,0.0004195747,0.0003406615,0.0007176395,0.001368781,0.001385443,0.00297493,0.01356024],"category_scores_gemma":[0.0113803,0.0003479797,0.0005413964,0.0002508427,0.0002987697,0.00216851,0.0008165851,0.001459095,0.002772075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005625646,"about_ca_system_score_gemma":0.0005606739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006614578,"about_ca_topic_score_gemma":0.0162789,"domain_scores_codex":[0.9992629,0.0004262332,0.00003893622,0.0001223409,0.0001071572,0.00004243299],"domain_scores_gemma":[0.9956604,0.003091032,0.0001372671,0.0004465372,0.0004300027,0.0002347901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006248555,0.002553863,0.06399532,0.001838802,0.0006525179,0.004265377,0.02585693,0.04010693,0.05491009,0.04179035,0.1058276,0.6519536],"study_design_scores_gemma":[0.0005324856,0.001293736,0.02340968,0.000317363,0.0007918259,0.002393407,0.00373942,0.8191188,0.02001497,0.02972927,0.09828538,0.000373701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3870751,0.001023273,0.5742792,0.005383649,0.0003736752,0.0007139025,0.002076547,0.0152763,0.01379823],"genre_scores_gemma":[0.7256007,0.0003804305,0.2551301,0.0005728205,0.00009714573,0.0002082213,0.001632127,0.0004629117,0.01591557],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01356024,"threshold_uncertainty_score":0.04536349,"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."}}