{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002384064,0.0001403465,0.000127817,0.0002112316,0.0002338601,0.0001411808,0.0001402278,0.0000718123,0.0005308338],"category_scores_gemma":[0.00001352025,0.0001368023,0.00008135329,0.0001666279,0.00005199984,0.001464788,0.00004843587,0.0001140231,0.00006771764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001530028,"about_ca_system_score_gemma":0.00009684992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002685931,"about_ca_topic_score_gemma":0.0003542884,"domain_scores_codex":[0.9991128,0.000003302561,0.0002105269,0.0002292065,0.0002220387,0.000222139],"domain_scores_gemma":[0.9995062,0.00003598977,0.0001624984,0.00008916043,0.000194816,0.00001132684],"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":[0.0001410812,0.00008431033,0.0135508,0.001392324,0.000106161,0.000004126087,0.0008295152,0.0001225483,0.4283982,0.5386558,0.008810531,0.007904661],"study_design_scores_gemma":[0.001890275,0.000002963302,0.001423312,0.0000770902,0.00009418978,0.000005379433,0.002165476,0.294702,0.0002914168,0.002389217,0.6964008,0.0005579584],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9608271,0.00007741428,0.00432629,0.0007736231,0.002416817,0.0004080106,0.000002909576,0.0002454033,0.03092247],"genre_scores_gemma":[0.9741516,0.000002888948,0.01744451,0.005533671,0.001381833,0.00003577594,0.0001516189,0.00003926041,0.00125883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6875902,"threshold_uncertainty_score":0.5812258,"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."}}