{"id":"W3097342189","doi":"10.3390/sym12111805","title":"Detecting Shilling Attacks Using Hybrid Deep Learning Models","year":2020,"lang":"en","type":"article","venue":"Symmetry","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Ryerson University","keywords":"Computer science; Deep learning; Robustness (evolution); Artificial intelligence; Convolutional neural network; Machine learning; Architecture; Recommender system; Attack model; Artificial neural network; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001061684,0.0009444337,0.0009002271,0.0007940707,0.000243411,0.0008051589,0.0009961985,0.0007482119,0.0007109074],"category_scores_gemma":[0.00272572,0.0004136748,0.0006001916,0.0004427073,0.0003559968,0.001477824,0.0006812941,0.001203291,0.0003553936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009229432,"about_ca_system_score_gemma":0.0005697895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009189838,"about_ca_topic_score_gemma":0.01193628,"domain_scores_codex":[0.9994237,0.0001269122,0.00004066781,0.0001479689,0.0001671556,0.00009361125],"domain_scores_gemma":[0.9986179,0.0004772811,0.0002518779,0.0002229068,0.0003676017,0.0000624103],"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":[0.0004092133,0.0003321982,0.01402054,0.0001076683,0.0002658759,0.0001690949,0.00009823179,0.6737198,0.01056319,0.004048139,0.003088375,0.2931777],"study_design_scores_gemma":[0.000002426887,0.00002318823,0.0003413214,0.000002193658,0.000007013007,0.000008655854,0.000002962652,0.9983778,0.0006761387,0.0004821882,0.00007279863,0.000003310566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2395161,0.001359762,0.7519627,0.0006520991,0.00008359399,0.0000915998,0.000337537,0.002537021,0.003459633],"genre_scores_gemma":[0.9592565,0.0002595145,0.0373042,0.0001449181,0.00002817598,0.00002757021,0.0003148729,0.00002606807,0.002638213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009189838,"threshold_uncertainty_score":0.0182727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0781143189336796,"score_gpt":0.2719299946488173,"score_spread":0.1938156757151377,"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."}}