{"id":"W4206588201","doi":"10.1109/bigdata52589.2021.9671467","title":"Deep Neural Network to Tradeoff between Accuracy and Diversity in a News Recommender System","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Big Data (Big Data)","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Science and Engineering Research Council","keywords":"Computer science; Recommender system; Representation (politics); Term (time); Reading (process); Artificial intelligence; Information retrieval; Taxonomy (biology); Process (computing); Diversity (politics); Domain (mathematical analysis); Linguistics; Political 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":[0.002411613,0.0009221986,0.001183834,0.0006299948,0.0005990021,0.001025385,0.001479055,0.001987471,0.001334065],"category_scores_gemma":[0.008845704,0.0004891796,0.0004175659,0.0006591728,0.0004874239,0.002818847,0.001138205,0.002154308,0.0004340991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001551212,"about_ca_system_score_gemma":0.00092316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01297494,"about_ca_topic_score_gemma":0.01432555,"domain_scores_codex":[0.9990892,0.0002311222,0.00006722719,0.0002752632,0.0001736402,0.0001635395],"domain_scores_gemma":[0.9967867,0.002001439,0.0001607182,0.0002036633,0.0007238921,0.0001235713],"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":[0.0006686633,0.0003682125,0.007533468,0.0001551378,0.0001805645,0.0001844458,0.0001788989,0.7728886,0.006545687,0.003255166,0.003402479,0.2046387],"study_design_scores_gemma":[0.00001192595,0.00004396392,0.0003138053,0.000005332381,0.00001391614,0.00001645942,0.000009324161,0.9974184,0.0008731816,0.001163552,0.0001254137,0.000004821789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5724329,0.003713074,0.4104821,0.002431479,0.0002121634,0.00009472609,0.0005264948,0.002150822,0.007956277],"genre_scores_gemma":[0.9649794,0.00023964,0.03129783,0.0003172139,0.00005957948,0.00003606139,0.0003171304,0.00003767951,0.002715482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01297494,"threshold_uncertainty_score":0.02579886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4290420453509283,"score_gpt":0.3636116226915197,"score_spread":0.06543042265940857,"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."}}