{"id":"W3022522270","doi":"10.1109/tcss.2020.2987846","title":"Deep Correlation Mining Based on Hierarchical Hybrid Networks for Heterogeneous Big Data Recommendations","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Social Systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":282,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"Natural Science Foundation of Hunan Province","keywords":"Computer science; Big data; Artificial intelligence; Field (mathematics); Variety (cybernetics); Machine learning; Data mining; Artificial neural network; Focus (optics); Router; Deep learning","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":[0.001283988,0.0008446633,0.001104083,0.002055155,0.0007822746,0.0009759556,0.002088837,0.0009202202,0.001229043],"category_scores_gemma":[0.005286156,0.0006808375,0.001054858,0.002158892,0.0004845827,0.00206206,0.001080348,0.001249275,0.0003489882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001142703,"about_ca_system_score_gemma":0.001164154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02285628,"about_ca_topic_score_gemma":0.04340623,"domain_scores_codex":[0.998993,0.0002801127,0.00006765295,0.0003104163,0.0002402386,0.0001086192],"domain_scores_gemma":[0.9973859,0.001457284,0.0003061988,0.0003099495,0.0004284443,0.0001123529],"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":[0.0001382245,0.0002160507,0.008700843,0.0001062952,0.0002402855,0.0002076983,0.000175656,0.8131441,0.001796858,0.01455248,0.003413496,0.157308],"study_design_scores_gemma":[0.000002214622,0.000007558071,0.0001514588,0.000001858362,0.00000770659,0.000008157523,0.000005328281,0.9976823,0.0001271862,0.001878766,0.0001249057,0.000002537145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03860307,0.0004177712,0.9583258,0.0002888387,0.00003817806,0.00008701471,0.000250799,0.0007542345,0.001234379],"genre_scores_gemma":[0.721931,0.0003546917,0.272907,0.0002610909,0.00008546855,0.0001908361,0.000935031,0.00008321054,0.003251536],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02285628,"threshold_uncertainty_score":0.04544646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08968234168746793,"score_gpt":0.2944058552688493,"score_spread":0.2047235135813814,"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."}}