{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000190203,0.0001415548,0.0001870195,0.00005501418,0.000242508,0.0002702315,0.000337555,0.00009338216,0.00001181331],"category_scores_gemma":[0.00003000479,0.0001257289,0.00007662365,0.0002567173,0.00002619668,0.0004997864,0.0002544664,0.0001503122,4.984058e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002046602,"about_ca_system_score_gemma":0.00001274973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005124901,"about_ca_topic_score_gemma":0.000003712404,"domain_scores_codex":[0.9989787,0.0000543153,0.0002329183,0.0003642352,0.00009686239,0.0002729504],"domain_scores_gemma":[0.9993119,0.0002085715,0.0001059075,0.0001692257,0.00004171658,0.0001626958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003525016,0.00004119091,0.009934609,0.0000852951,0.0001048582,0.00001587538,0.001264428,0.001416621,0.0001778543,0.9247969,0.01538518,0.04674193],"study_design_scores_gemma":[0.000176006,0.00007732485,0.0001259053,0.00002530912,0.000005435343,0.00001618683,0.00007950675,0.9840845,0.00005015618,0.01326958,0.001916101,0.0001740037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001142253,0.0001197477,0.9929552,0.004589453,0.000443462,0.0003097031,0.000003802,0.0002857227,0.0001506203],"genre_scores_gemma":[0.6846344,0.00001220378,0.3132269,0.001905309,0.0001883471,0.00001660256,0.00000212885,0.000007993465,0.000006073793],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9826679,"threshold_uncertainty_score":0.5127077,"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."}}