{"id":"W4412510185","doi":"10.4236/jsea.2025.187016","title":"AI-Powered Personalization in SuperApps: International Case Studies on User Engagement","year":2025,"lang":"en","type":"article","venue":"Journal of Software Engineering and Applications","topic":"Technology Use by Older Adults","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Personalization; Computer science; User engagement; Human–computer interaction; World Wide Web; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000347335,0.00005659774,0.0000957515,0.0002851757,0.0001261348,0.00002494218,0.0001044896,0.00004988026,0.000005030615],"category_scores_gemma":[0.0004860213,0.0000553658,0.00002470821,0.0002635249,0.00004648196,0.00009886197,0.00002207151,0.0001871529,8.738564e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001204823,"about_ca_system_score_gemma":0.00005549722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001707132,"about_ca_topic_score_gemma":0.00006189933,"domain_scores_codex":[0.9994992,0.00002039701,0.0002058101,0.00008175832,0.0001118954,0.00008094588],"domain_scores_gemma":[0.9995284,0.0001868502,0.00005614761,0.00005832931,0.0001427818,0.00002748463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007095173,0.0009546131,0.1676703,0.0003658744,0.001105195,0.0005776722,0.07097945,0.02125395,0.0008705265,0.6693647,0.009947528,0.05683924],"study_design_scores_gemma":[0.003798617,0.0002010187,0.0413729,0.001723217,0.0001862236,0.000422544,0.06048852,0.001503366,0.001024061,0.008598696,0.8799172,0.0007636516],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8051433,0.002435123,0.1745035,0.015873,0.000956636,0.0005955238,0.00001457444,0.0002354387,0.0002428589],"genre_scores_gemma":[0.9959265,0.0005419627,0.003111061,0.0001598367,0.00008744869,0.00003849547,9.550521e-7,0.000004511218,0.0001292151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8699697,"threshold_uncertainty_score":0.2257752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01610190427526705,"score_gpt":0.3252762164847916,"score_spread":0.3091743122095246,"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."}}