{"id":"W2103960538","doi":"10.1109/wi.2005.147","title":"Time Based Segmentation of Log Data for User Navigation Prediction in Personalization","year":2005,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Computer science; Personalization; The Internet; Sample (material); Data mining; Segmentation; Market segmentation; World Wide Web; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001032693,0.00003883418,0.00006888872,0.0000822143,0.0001221515,0.00001767865,0.0001198506,0.00005177682,0.001116152],"category_scores_gemma":[0.0001429656,0.00004097396,0.00002486458,0.0002696726,0.00007120331,0.0004215427,0.000006583239,0.00002290919,0.00001802336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001188573,"about_ca_system_score_gemma":0.0001340996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001754263,"about_ca_topic_score_gemma":0.01065249,"domain_scores_codex":[0.999239,0.0001118339,0.0002060631,0.0001602346,0.0002027351,0.00008009704],"domain_scores_gemma":[0.9995013,0.0001217406,0.00007586208,0.0001576369,0.0001203369,0.00002305688],"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":[0.0005072183,0.003540779,0.240906,0.0007796937,0.0002244071,5.390393e-7,0.08784331,0.2788789,0.02989406,0.03742807,0.03860084,0.2813962],"study_design_scores_gemma":[0.0005584313,0.00003084998,0.004346996,0.00003085291,0.00004318541,1.805479e-8,0.003079631,0.9823447,0.00115136,0.0002329308,0.008102745,0.00007833206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4070551,0.00003842543,0.5832654,0.004092222,0.00006920139,0.001225594,0.0002709982,0.00009959175,0.003883508],"genre_scores_gemma":[0.9929037,0.000003068885,0.002722464,0.0001125441,0.0001058684,0.00002548154,0.00262382,0.000003632179,0.001499405],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7034658,"threshold_uncertainty_score":0.999797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03795026139339331,"score_gpt":0.3395161890645997,"score_spread":0.3015659276712064,"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."}}