{"id":"W4387712003","doi":"10.1109/acit58437.2023.10275513","title":"Recency-Frequency-Monetary Analysis and Recommendation System using Apriori Algorithm on E-Commerce Sales Data","year":2023,"lang":"en","type":"article","venue":"","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Apriori algorithm; Association rule learning; Computer science; Python (programming language); Revenue; Recommender system; A priori and a posteriori; Data mining; Product (mathematics); Order (exchange); Affinity analysis; World Wide Web; Data science; Information retrieval; Finance; Operating system","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":[0.00201705,0.001088881,0.0009615761,0.003876142,0.0006382032,0.001579526,0.001333776,0.0007012088,0.003946897],"category_scores_gemma":[0.006147414,0.000612385,0.001177136,0.003097643,0.0002032509,0.001491663,0.0005581144,0.001186205,0.003922402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007620314,"about_ca_system_score_gemma":0.001507075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01176921,"about_ca_topic_score_gemma":0.01275139,"domain_scores_codex":[0.9992102,0.00009545992,0.000149608,0.0002500784,0.0002383206,0.00005625163],"domain_scores_gemma":[0.9980804,0.0007853904,0.0001652343,0.0002665845,0.0006102937,0.00009215735],"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":[0.001190331,0.0007203904,0.03804338,0.0006085022,0.0004915225,0.001252247,0.0004202352,0.04634897,0.01211258,0.003438048,0.046227,0.8491468],"study_design_scores_gemma":[0.0001101049,0.0002170218,0.006108833,0.00005479012,0.0001156816,0.0005051434,0.0001273068,0.9592702,0.01730498,0.003782623,0.01233462,0.00006871276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1234662,0.001270219,0.6814842,0.0009628212,0.000248137,0.001114635,0.02181028,0.1635835,0.006060026],"genre_scores_gemma":[0.2032269,0.0007644237,0.7724163,0.000221053,0.00006943851,0.0004569365,0.01754817,0.0007052787,0.004591544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01176921,"threshold_uncertainty_score":0.02340138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0811913316605252,"score_gpt":0.2951596630560771,"score_spread":0.2139683313955519,"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."}}