{"id":"W2952487561","doi":"","title":"Toward Experiential Utility Elicitation for Interface Customization","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Personalization; Preference elicitation; Computer science; Experiential learning; Requirements elicitation; Interface (matter); Human–computer interaction; Presentation (obstetrics); User interface; Expert elicitation; Quality (philosophy); Domain (mathematical analysis); Ask price; Software; Preference; Knowledge management; World Wide Web; Requirements engineering; Psychology","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":[0.02365176,0.001524162,0.0006498682,0.001892509,0.0006970891,0.003534576,0.00219073,0.001710935,0.003093231],"category_scores_gemma":[0.1005523,0.0009469249,0.0008620207,0.001799391,0.003062856,0.005102954,0.004450109,0.002844192,0.0006176764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002056867,"about_ca_system_score_gemma":0.002314141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001550207,"about_ca_topic_score_gemma":0.001957621,"domain_scores_codex":[0.9721787,0.02194491,0.001051259,0.001460579,0.003031029,0.0003334645],"domain_scores_gemma":[0.899483,0.0789101,0.005095145,0.01045879,0.005261534,0.0007914856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005562479,0.001659227,0.01838171,0.001279709,0.0002272004,0.0002632359,0.01216555,0.1189962,0.01786826,0.4852364,0.00321495,0.3401514],"study_design_scores_gemma":[0.0001111402,0.0003178015,0.004235251,0.0002737967,0.00003641495,0.0001628303,0.001704973,0.5256075,0.01028903,0.4502575,0.006880288,0.0001234782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01762126,0.00005156192,0.9785594,0.000492257,0.000007503228,0.0002485327,0.00009809082,0.000169678,0.002751619],"genre_scores_gemma":[0.2877342,0.0001359304,0.7103317,0.0001840446,0.00001730879,0.0008442381,0.0001992841,0.00003735154,0.0005159428],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02365176,"threshold_uncertainty_score":0.125084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5183484482689067,"score_gpt":0.3558588631991785,"score_spread":0.1624895850697282,"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."}}