{"id":"W3009189291","doi":"10.1109/globecom38437.2019.9014224","title":"Adaptive Content Placement in Edge Networks Based on Hybrid User Preference Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Backhaul (telecommunications); Stochastic gradient descent; Enhanced Data Rates for GSM Evolution; Hybrid learning; Latency (audio); Gradient descent; Cloud computing; Artificial intelligence; Content delivery; Artificial neural network; Machine learning; Computer network; Base station","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":[0.0002984496,0.0001349455,0.0001535522,0.00009911422,0.00004535429,0.00009274745,0.0003784493,0.00003143012,0.00007282192],"category_scores_gemma":[0.00001702575,0.0001132577,0.0000548231,0.0001307276,0.0000107826,0.0001940428,0.0001418325,0.000310735,0.0001768637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000872188,"about_ca_system_score_gemma":0.00004157893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000157855,"about_ca_topic_score_gemma":0.0000319908,"domain_scores_codex":[0.9987819,0.0001167785,0.0001738521,0.0004061399,0.0002410218,0.0002802831],"domain_scores_gemma":[0.9993038,0.0001719695,0.00005554063,0.0003594315,0.00004705791,0.0000622313],"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":[0.0002304424,0.0002461692,0.03149546,0.000008460143,0.00002040765,0.00003401821,0.0001834228,0.9288673,0.0003699615,0.01289489,0.001762292,0.02388712],"study_design_scores_gemma":[0.0007936931,0.0003284738,0.004578977,0.00009689404,0.000001559981,0.00000106711,0.00007158567,0.9931267,0.0002125957,0.00001592051,0.000607844,0.0001647141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3079159,0.00004885726,0.6762952,0.0002881954,0.0004891368,0.0003394637,5.163336e-7,0.0001725748,0.01445018],"genre_scores_gemma":[0.9911437,0.000005466008,0.0006793659,0.001010243,0.0000213571,0.00001667387,0.000002563975,0.0000063728,0.007114273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6832278,"threshold_uncertainty_score":0.4618514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04732878300822285,"score_gpt":0.2101738533164994,"score_spread":0.1628450703082766,"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."}}