{"id":"W3080884086","doi":"10.1145/3394486.3403254","title":"A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Recommender system; Machine learning; Inference; Convolutional neural network; Graph; Artificial intelligence; Collaborative filtering; Data mining; Theoretical computer science","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":[0.001092126,0.001229731,0.001127028,0.001597001,0.0005271623,0.001040617,0.002960369,0.001892932,0.002733633],"category_scores_gemma":[0.00341031,0.001037413,0.0009470207,0.00195758,0.0007340309,0.001987772,0.0009281315,0.002177955,0.001163093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002325752,"about_ca_system_score_gemma":0.001816529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07485351,"about_ca_topic_score_gemma":0.08838239,"domain_scores_codex":[0.9993786,0.0001471077,0.00002928759,0.0001850288,0.0001849136,0.00007505542],"domain_scores_gemma":[0.9992478,0.0003429725,0.00007091188,0.0000799097,0.0002096422,0.00004884642],"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":[0.0001056558,0.00007731389,0.001270982,0.00009395124,0.0001057087,0.00008910323,0.00006983329,0.833401,0.001289155,0.02524421,0.006271019,0.131982],"study_design_scores_gemma":[0.000004852384,0.000007276244,0.00008258092,0.00000530439,0.000007067782,0.000008562765,0.000002181955,0.9940854,0.0001181792,0.005228848,0.000445325,0.000004441373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007258046,0.0007858776,0.9884669,0.0004709502,0.00005123209,0.00005103771,0.0003764529,0.0008775747,0.00166198],"genre_scores_gemma":[0.4755707,0.001929903,0.5031967,0.0008430141,0.0002903954,0.0003933361,0.002291903,0.0002527279,0.01523126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07485351,"threshold_uncertainty_score":0.1488356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09558159788905851,"score_gpt":0.3135492172801265,"score_spread":0.217967619391068,"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."}}