{"id":"W2110935116","doi":"10.1109/icsssm.2010.5530273","title":"Classifying customers using navigational history for developing personalized Web services","year":2010,"lang":"en","type":"article","venue":"","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Personalized marketing; Plan (archaeology); Personalization; World Wide Web; Web service; Promotion (chess); Digital 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":[0.0005371363,0.0004543404,0.0004768114,0.003114006,0.0005619424,0.001289114,0.0005323609,0.0005106464,0.001716829],"category_scores_gemma":[0.002130996,0.000275192,0.0003366996,0.001963695,0.0002417241,0.001376048,0.0004588534,0.0005524502,0.0007625773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007520008,"about_ca_system_score_gemma":0.001043606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01262647,"about_ca_topic_score_gemma":0.02226878,"domain_scores_codex":[0.9995338,0.0001164579,0.00003574273,0.00008380113,0.0001569817,0.00007337515],"domain_scores_gemma":[0.9991338,0.0003055358,0.0001069447,0.00009964577,0.0002793593,0.00007478142],"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":[0.0002725097,0.0006087276,0.09629247,0.0001448305,0.00009438527,0.0002360697,0.00101938,0.02551555,0.01826975,0.007845623,0.005680222,0.8440205],"study_design_scores_gemma":[0.00002675582,0.0002600005,0.0478966,0.00005189082,0.0001181499,0.0004696994,0.001019116,0.914127,0.01734525,0.008943547,0.009618173,0.0001237209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4670049,0.000592559,0.5116374,0.0006355966,0.00003697097,0.0005344958,0.001174893,0.004381758,0.01400131],"genre_scores_gemma":[0.765592,0.0001792497,0.2304507,0.00006382902,0.00003087058,0.0001142333,0.0009855223,0.00008140175,0.002502199],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01262647,"threshold_uncertainty_score":0.02510595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0490659853035058,"score_gpt":0.2746891812264154,"score_spread":0.2256231959229096,"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."}}