{"id":"W4417249539","doi":"10.1109/ece67147.2025.11276646","title":"Bridging the Generation Gap: Age-Parity in Synthetic Customer Profiles","year":2025,"lang":"","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Marriott International (Canada)","funders":"","keywords":"Bridging (networking); Autoencoder; Baseline (sea); Transaction data; Personalization; Synthetic data; Discriminative model; Analytics; Benchmark (surveying)","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":[0.003186571,0.0003784679,0.0003885634,0.0004475319,0.0003741188,0.0007096863,0.0009914001,0.00091706,0.001132888],"category_scores_gemma":[0.01268976,0.0003040458,0.0004380034,0.0004877453,0.0004835277,0.001097043,0.001060389,0.001117065,0.0004317101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007411265,"about_ca_system_score_gemma":0.0004646458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004763973,"about_ca_topic_score_gemma":0.005836913,"domain_scores_codex":[0.9990667,0.0005087923,0.00002957756,0.0002014124,0.0001038094,0.00008979897],"domain_scores_gemma":[0.9955248,0.002974061,0.0002334614,0.000727612,0.0003969957,0.0001431805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009559118,0.0004447377,0.09244773,0.0001071443,0.0001185576,0.0003702872,0.0009352043,0.7318001,0.00510416,0.02006318,0.0155924,0.1320607],"study_design_scores_gemma":[0.00002100726,0.00003805578,0.004463153,0.00001017263,0.000007292334,0.00004984195,0.00008838003,0.9857862,0.001544669,0.006468507,0.001509217,0.00001348619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7775198,0.000290553,0.2146038,0.001116092,0.0001343051,0.0001410386,0.002831541,0.0007682169,0.002594607],"genre_scores_gemma":[0.967616,0.00005282071,0.02717,0.0002547209,0.0000311162,0.00008062334,0.003600308,0.00005794463,0.001136556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004763973,"threshold_uncertainty_score":0.01685238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04344859735570326,"score_gpt":0.2912691484230089,"score_spread":0.2478205510673057,"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."}}