{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001090873,0.0002667284,0.0004131178,0.0003369535,0.00084955,0.00724173,0.001484667,0.0001728054,0.000001845191],"category_scores_gemma":[0.00006598898,0.0002342978,0.00005782989,0.0002078969,0.00004781011,0.01228592,0.0002738418,0.0002946656,0.00003751385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001839803,"about_ca_system_score_gemma":0.0001103578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001081485,"about_ca_topic_score_gemma":0.00001746478,"domain_scores_codex":[0.997522,0.0001409475,0.001088393,0.000292326,0.0004940561,0.0004622768],"domain_scores_gemma":[0.9973373,0.00004073627,0.001235227,0.001011711,0.0002559824,0.0001190643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008898976,0.0004374054,0.05134796,0.01291977,0.0001535785,0.000146866,0.02821479,0.01035913,0.001751473,0.1890493,0.1190547,0.5864761],"study_design_scores_gemma":[0.0008026102,0.00007847313,0.001978395,0.001448859,0.000004782539,0.0001343122,0.0007747171,0.9053835,0.0004240778,0.0001161393,0.08827975,0.0005743564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03622263,0.002722729,0.8579081,0.00477258,0.0131899,0.003897647,0.00003036396,0.002771472,0.07848459],"genre_scores_gemma":[0.9981313,0.00001106715,0.001042102,0.000182353,0.0001445684,0.0001803676,0.000008007919,0.0000146902,0.0002854949],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9619087,"threshold_uncertainty_score":0.9937888,"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."}}