{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001171584,0.00007662167,0.0001222869,0.000153041,0.00004543557,0.00006097125,0.00006987393,0.00002534602,0.005010571],"category_scores_gemma":[0.000011953,0.0000651818,0.00004526758,0.0002157669,0.00007492177,0.000355366,0.00004044291,0.0000377312,0.0001422667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006178608,"about_ca_system_score_gemma":0.000005873731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001640601,"about_ca_topic_score_gemma":0.00008546312,"domain_scores_codex":[0.999493,0.000004555578,0.0001902967,0.00009669331,0.0001152372,0.0001002391],"domain_scores_gemma":[0.9997168,0.00002268501,0.000109731,0.00008238198,0.00006588387,0.000002506478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003830426,0.0001152318,0.6598479,0.0004148972,0.00003254483,0.000003881974,0.0000475271,0.000006319048,0.0125966,0.08651289,0.007935689,0.2324482],"study_design_scores_gemma":[0.003545759,0.00001540675,0.7495011,0.0003080991,0.0007333058,0.00003042623,0.002530434,0.02307743,0.007712441,0.009210228,0.2021507,0.001184635],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8644378,0.00004402849,0.0001849259,0.0001064738,0.0001789009,0.00006875587,3.9006e-7,0.0001033699,0.1348753],"genre_scores_gemma":[0.9981476,0.000001262058,0.000686792,0.00007242754,0.0001097551,0.000003471585,0.000005103304,0.000007471352,0.0009661004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2312636,"threshold_uncertainty_score":0.995899,"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."}}