{"id":"W1977102366","doi":"10.1145/1111449.1111501","title":"Automatic construction of personalized customer interfaces","year":2006,"lang":"en","type":"article","venue":"","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Personalization; Human–computer interaction; User interface; Inference; User modeling; Interface (matter); User interface design; Context (archaeology); Simple (philosophy); Natural user interface; User experience design; Artificial intelligence; World Wide Web; Programming language","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":[0.001424088,0.0007480953,0.0007059826,0.00127581,0.0005282719,0.002157799,0.00127117,0.001234004,0.008731816],"category_scores_gemma":[0.01184796,0.0007337659,0.0008364071,0.001212245,0.0004230905,0.002088411,0.001499913,0.001117193,0.002841766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006529587,"about_ca_system_score_gemma":0.0007982498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001770179,"about_ca_topic_score_gemma":0.002636383,"domain_scores_codex":[0.9983519,0.0005324276,0.00007985397,0.0003808725,0.0005422907,0.0001126456],"domain_scores_gemma":[0.9959806,0.001651259,0.0002799259,0.001382617,0.0006236405,0.00008181702],"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":[0.0004113561,0.0003299302,0.01086382,0.0004344037,0.000111908,0.0003640276,0.0009327598,0.05478875,0.02794868,0.03006459,0.02819549,0.8455543],"study_design_scores_gemma":[0.00006888573,0.0001125054,0.007232187,0.00006174205,0.00007633853,0.0004473633,0.0003155711,0.8861188,0.02973383,0.04346452,0.03227575,0.00009249639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04635514,0.0002419243,0.9243353,0.0002687734,0.00007412396,0.0003586197,0.001093148,0.01818564,0.00908735],"genre_scores_gemma":[0.4370532,0.0002806313,0.5532878,0.0001475835,0.00004748103,0.0003524309,0.002431619,0.001499911,0.004899388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008731816,"threshold_uncertainty_score":0.02921081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0128241307914343,"score_gpt":0.2258960186968782,"score_spread":0.2130718879054439,"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."}}