{"id":"W3086645187","doi":"10.1109/iri49571.2020.00045","title":"Using a Deep Learning Model, Content Features, and Author Metadata to Recommend Research Papers","year":2020,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Metadata; Popularity; Quality (philosophy); Jungle; World Wide Web; Publishing; Recommender system; Information retrieval; Data science; Collaborative filtering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001926036,0.0009239039,0.001139043,0.004124823,0.0004880341,0.001292147,0.001718954,0.002045713,0.001772467],"category_scores_gemma":[0.005577632,0.0004877913,0.001181163,0.003534007,0.0003558343,0.002049893,0.0006252424,0.001366154,0.00158586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001258948,"about_ca_system_score_gemma":0.001948089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01703662,"about_ca_topic_score_gemma":0.03230178,"domain_scores_codex":[0.9992985,0.000143939,0.00008215747,0.0002130393,0.0001829647,0.00007943051],"domain_scores_gemma":[0.9969117,0.001563795,0.00023437,0.0002638366,0.0008788452,0.0001474792],"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":[0.0007010587,0.001302927,0.03489913,0.0006457554,0.0006385219,0.0002760438,0.0002467035,0.1585075,0.006849262,0.0040664,0.03062847,0.7612383],"study_design_scores_gemma":[0.00004976269,0.0001313214,0.001970019,0.00005317395,0.0001182102,0.00008823864,0.00002749839,0.9907221,0.001375925,0.003097079,0.002340304,0.00002639425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2343442,0.01011614,0.7295539,0.004169303,0.001216213,0.0005653812,0.004948829,0.006426999,0.008659041],"genre_scores_gemma":[0.7900373,0.003473677,0.1834656,0.00106974,0.0009148318,0.0003867705,0.006659747,0.000132477,0.01385979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01703662,"threshold_uncertainty_score":0.03387493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4279885983222725,"score_gpt":0.4016992480998558,"score_spread":0.02628935022241674,"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."}}