{"id":"W2963699115","doi":"10.1109/icdmw.2018.00122","title":"Generating Realistic Sequences of Customer-Level Transactions for Retail Datasets","year":2018,"lang":"en","type":"article","venue":"","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McGill University","funders":"","keywords":"Computer science; Recurrent neural network; Task (project management); Database transaction; Transaction data; Purchasing; Artificial intelligence; Machine learning; Data mining; Artificial neural network; Database; Marketing","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":[0.001727585,0.001017988,0.0005862712,0.001289126,0.0003839707,0.000879812,0.002078721,0.002134729,0.003002744],"category_scores_gemma":[0.007047857,0.0005485589,0.001226178,0.00148081,0.0006950453,0.001268739,0.001057654,0.002400612,0.002018565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001079651,"about_ca_system_score_gemma":0.0005452436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004096343,"about_ca_topic_score_gemma":0.01075223,"domain_scores_codex":[0.9987586,0.000500397,0.0000811792,0.0003611593,0.0002033848,0.00009532309],"domain_scores_gemma":[0.9971707,0.001531589,0.0002172254,0.000624944,0.0003337997,0.0001218101],"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":[0.001099109,0.001819447,0.07247401,0.0006796606,0.0004419438,0.0009666948,0.000276755,0.7521771,0.003879116,0.00768066,0.0665494,0.09195615],"study_design_scores_gemma":[0.00008260756,0.0002053906,0.01073416,0.00003249107,0.00002493321,0.000237484,0.0001050457,0.9745432,0.001684557,0.005572553,0.006737615,0.00004011916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7145602,0.002233456,0.1581473,0.002653745,0.0005843934,0.0007865891,0.1082863,0.006735673,0.006012349],"genre_scores_gemma":[0.6896356,0.000586387,0.09327912,0.0006261212,0.0001296389,0.0005861991,0.2114211,0.0002133589,0.003522492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004096343,"threshold_uncertainty_score":0.01004517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0884931706702073,"score_gpt":0.2961337664315699,"score_spread":0.2076405957613626,"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."}}