{"id":"W7117873310","doi":"10.5281/zenodo.18107972","title":"Customer Product Choice Recommendation By Association Rules And Learning Models","year":2025,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sciencetech (Canada)","funders":"","keywords":"Purchasing; Association rule learning; Boosting (machine learning); Ensemble learning; Product (mathematics); Recommender system; Customer intelligence; Normalization (sociology)","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":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001935917,0.0002861427,0.0002674669,0.0006899692,0.004961303,0.00426022,0.0004921891,0.0001356985,0.007818661],"category_scores_gemma":[0.001758566,0.0003442599,0.00006548429,0.001228617,0.00009211963,0.002589119,0.001177662,0.0006515053,0.004880624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005130728,"about_ca_system_score_gemma":0.00000583137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001927824,"about_ca_topic_score_gemma":0.000001631912,"domain_scores_codex":[0.9973857,0.000374913,0.0005171495,0.0007510741,0.0004476355,0.0005234958],"domain_scores_gemma":[0.9979831,0.00008150128,0.0004923858,0.0002566356,0.001134981,0.00005141587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008436137,0.0002319064,0.0002283529,0.0004930956,0.0001653941,9.648506e-7,0.0006136233,0.0001842695,0.00280347,0.003783125,0.5649888,0.4264227],"study_design_scores_gemma":[0.001025032,0.00003173912,0.001247104,0.0001102415,0.0001353772,0.000002844788,0.0007275146,0.01450558,0.0001133945,0.0003791462,0.9814006,0.0003214305],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.100641,0.001647109,0.0121938,0.0231064,0.001412902,0.002637567,0.0002716438,0.002078928,0.8560106],"genre_scores_gemma":[0.9622945,0.001119746,0.0001136823,0.001362634,0.0007008209,2.798267e-7,0.009472919,0.001190687,0.02374479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8616534,"threshold_uncertainty_score":0.9999009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03121921628926136,"score_gpt":0.2426882272889796,"score_spread":0.2114690109997182,"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."}}