{"id":"W2896581050","doi":"10.1109/ijcnn.2018.8489194","title":"Using Deep Learning to Recommend Discussion Threads to Users in an Online Forum","year":2018,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Latent Dirichlet allocation; Computer science; Set (abstract data type); Recall; Conversation; Artificial neural network; Test set; Topic model; Artificial intelligence; Probabilistic logic; Sample (material); Social media; Ideal (ethics); Machine learning; F1 score; Precision and recall; Data set; Test (biology); World Wide Web","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":[0.002923411,0.00116594,0.0004922769,0.002438081,0.0006864053,0.001313269,0.001013406,0.001233396,0.001271367],"category_scores_gemma":[0.00938681,0.0005575021,0.0006682351,0.001319041,0.0003079113,0.002489114,0.001046274,0.001732977,0.0008323602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001589846,"about_ca_system_score_gemma":0.001391298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01388609,"about_ca_topic_score_gemma":0.02803006,"domain_scores_codex":[0.9990006,0.00040002,0.00007028055,0.0002698935,0.0001390445,0.0001200805],"domain_scores_gemma":[0.9954397,0.002804039,0.0003657157,0.0003232886,0.000839639,0.000227576],"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":[0.001015783,0.001687501,0.04873134,0.0004008342,0.0003140829,0.0002179582,0.001957957,0.1449527,0.009798571,0.003679233,0.01142344,0.7758205],"study_design_scores_gemma":[0.00003191961,0.0001081037,0.002603026,0.00002813347,0.00003546469,0.00002695237,0.0001317818,0.9894807,0.002385605,0.003749645,0.001401501,0.00001702902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5245891,0.001658839,0.4564184,0.001626985,0.0003381495,0.0005327365,0.001621212,0.005788346,0.007426187],"genre_scores_gemma":[0.8946495,0.000287237,0.09919301,0.0001450219,0.00008093775,0.0001747732,0.001526232,0.0000598237,0.003883386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01388609,"threshold_uncertainty_score":0.02761054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09314019694162755,"score_gpt":0.3452712908945826,"score_spread":0.2521310939529551,"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."}}