{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002054345,0.00009606091,0.0001088576,0.0001763976,0.0001081881,0.0001074322,0.0005128673,0.0000394008,0.00003320257],"category_scores_gemma":[0.00004159505,0.00006678631,0.00001848857,0.0004000029,0.000007840848,0.0004923093,0.0004825706,0.0001102577,0.00003154079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007925832,"about_ca_system_score_gemma":0.00001940313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002887984,"about_ca_topic_score_gemma":0.005751561,"domain_scores_codex":[0.9988856,0.00005578048,0.0001881577,0.0003905505,0.0001478395,0.0003321295],"domain_scores_gemma":[0.999347,0.00001744804,0.00002538686,0.0003975779,0.00004194027,0.0001706608],"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":[0.00001579617,0.00009658863,0.05350901,0.00000412761,0.00000319294,0.000006273939,0.008093962,0.03288629,0.006902149,0.003150522,0.0001269139,0.8952051],"study_design_scores_gemma":[0.0001352875,0.0002025377,0.002757033,0.00003788345,8.675252e-7,0.000002651387,0.001081035,0.9919757,0.0006815859,0.0006443039,0.002314026,0.0001670708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4045058,0.00000149798,0.5903006,0.004673282,0.000168103,0.00007780838,1.199145e-7,0.00006679486,0.00020606],"genre_scores_gemma":[0.5616176,2.570663e-7,0.4367469,0.001394215,0.00007575031,0.000001646521,7.270791e-7,0.000006350755,0.0001565735],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9590894,"threshold_uncertainty_score":0.3209506,"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."}}