{"id":"W4210781813","doi":"10.1155/2022/3843021","title":"Research on Recommendation Algorithm of Joint Light Graph Convolution Network and DropEdge","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Overfitting; Computer science; Learning to rank; Embedding; Convolutional neural network; Graph; Artificial intelligence; Machine learning; Rank (graph theory); Pattern recognition (psychology); Data mining; Algorithm; Artificial neural network; Theoretical computer science; Ranking (information retrieval); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001119648,0.001467099,0.001471745,0.0009112048,0.0005150116,0.001149013,0.002878364,0.001859089,0.00330262],"category_scores_gemma":[0.003775127,0.0007268674,0.001119597,0.001461855,0.0005434616,0.003509699,0.0009894067,0.002294403,0.001034931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001627661,"about_ca_system_score_gemma":0.001502012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0237806,"about_ca_topic_score_gemma":0.02724211,"domain_scores_codex":[0.9992065,0.0001169042,0.00007063533,0.0002577349,0.0002332662,0.0001150133],"domain_scores_gemma":[0.9990108,0.0002996893,0.00007536003,0.0001998441,0.0003607601,0.00005357946],"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":[0.0002812871,0.000210303,0.003990011,0.0002122976,0.0002267826,0.000149171,0.00007722279,0.3757927,0.006016025,0.01165945,0.008916263,0.5924685],"study_design_scores_gemma":[0.00001719227,0.00003845945,0.0002672559,0.000008148868,0.00002189807,0.00005013475,0.000006788463,0.9948794,0.00156497,0.00248242,0.0006543586,0.000008991992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03289308,0.001913313,0.9575076,0.0006275497,0.0001357437,0.0001170165,0.0002843312,0.003179465,0.00334197],"genre_scores_gemma":[0.5673636,0.002646941,0.4078664,0.0009709274,0.0001665527,0.0003305338,0.001931171,0.0003847457,0.01833899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0237806,"threshold_uncertainty_score":0.04728431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03912457970409727,"score_gpt":0.3132798726267936,"score_spread":0.2741552929226964,"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."}}