{"id":"W3176116633","doi":"10.1109/wiiat50758.2020.00015","title":"A Streamlined News Recommender System Using Variable Markov Model","year":2020,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Science and Engineering Research Council","keywords":"Computer science; Recommender system; Tree traversal; Markov chain; Overhead (engineering); Variable (mathematics); Markov model; Markov process; World Wide Web; Information retrieval; Machine learning; Algorithm","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":[0.001338972,0.0005813661,0.001258271,0.0007034976,0.0007346995,0.001034328,0.001656519,0.001133014,0.002681628],"category_scores_gemma":[0.002992474,0.0005440045,0.0009479513,0.0009622496,0.0001976375,0.001561204,0.0007242282,0.00122228,0.001681527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007032932,"about_ca_system_score_gemma":0.001279197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02952348,"about_ca_topic_score_gemma":0.03355337,"domain_scores_codex":[0.9993398,0.0001694474,0.00004769012,0.0001992826,0.0001874316,0.00005644185],"domain_scores_gemma":[0.9984416,0.0006827008,0.000103129,0.0002623285,0.0003921729,0.0001181197],"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":[0.001745655,0.0007478718,0.009235183,0.0005597918,0.0004708748,0.0005916556,0.0005804699,0.3114942,0.03123377,0.01964245,0.02868877,0.5950092],"study_design_scores_gemma":[0.00006637284,0.0001336954,0.0004607019,0.000007212826,0.00005610457,0.00009239006,0.00002051462,0.9925696,0.001649493,0.00189963,0.003015678,0.00002867195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04604272,0.0008713519,0.9425449,0.0005470114,0.000181787,0.0002211663,0.0007809936,0.005976031,0.002834105],"genre_scores_gemma":[0.4721702,0.0008176019,0.5142371,0.0002924434,0.0001814737,0.0002360222,0.001800525,0.0001728602,0.01009175],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02952348,"threshold_uncertainty_score":0.0587033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06026746745316695,"score_gpt":0.2604852631332247,"score_spread":0.2002177956800577,"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."}}