{"id":"W2753686090","doi":"","title":"DropoutNet: Addressing Cold Start in Recommender Systems","year":2017,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":154,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cold start (automotive); Computer science; Recommender system; Scalability; Dropout (neural networks); Deep learning; Focus (optics); Artificial neural network; Artificial intelligence; Code (set theory); Machine learning; Deep neural networks; Database","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":[0.005234415,0.0015484,0.002626134,0.0007741285,0.001472978,0.001924807,0.004067247,0.003244734,0.005291685],"category_scores_gemma":[0.01427719,0.001282858,0.001385185,0.001124265,0.001433501,0.005240412,0.003587662,0.006096192,0.002146352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002541173,"about_ca_system_score_gemma":0.002233778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.016859,"about_ca_topic_score_gemma":0.03408351,"domain_scores_codex":[0.9974607,0.001091844,0.0001558033,0.0005862904,0.0004430723,0.0002622513],"domain_scores_gemma":[0.9933773,0.004005729,0.0004044374,0.001136925,0.0008038286,0.0002718998],"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":[0.001470795,0.0007288436,0.006957904,0.0007687781,0.000481571,0.0004163497,0.0006595356,0.6806151,0.004333635,0.02943872,0.0337466,0.2403822],"study_design_scores_gemma":[0.00003955828,0.00006961956,0.0001875606,0.00002022876,0.00002392091,0.00002814271,0.00001627724,0.9888483,0.0009634878,0.008670956,0.001119072,0.00001288326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05334657,0.002201137,0.9315229,0.001555339,0.0002508182,0.0002264383,0.001037056,0.005929776,0.003929933],"genre_scores_gemma":[0.7042405,0.0009465954,0.2720304,0.00180694,0.0002891803,0.0004917138,0.003854095,0.0009338735,0.01540662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.016859,"threshold_uncertainty_score":0.03352171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0726473000804211,"score_gpt":0.3102200947430976,"score_spread":0.2375727946626766,"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."}}