{"id":"W4248744920","doi":"10.32920/ryerson.14649402","title":"Streamlined News Recommendation System Using a Variable Markov Model","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Markov chain; Variable (mathematics); Tree traversal; Overhead (engineering); Control (management); Markov model; Markov process; World Wide Web; Information retrieval; Artificial intelligence; 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":[0.001151041,0.0004480498,0.001115794,0.0007706169,0.0006418221,0.001151489,0.001529929,0.0008188233,0.002747112],"category_scores_gemma":[0.002438223,0.0005419581,0.0009242984,0.0009803585,0.0001992276,0.001477356,0.0006539305,0.001146512,0.001524289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008277028,"about_ca_system_score_gemma":0.001175398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02641611,"about_ca_topic_score_gemma":0.03035682,"domain_scores_codex":[0.9994441,0.00011257,0.00004847702,0.0001734,0.0001615574,0.00005986946],"domain_scores_gemma":[0.9986042,0.0006711847,0.00009166834,0.0002256865,0.000301181,0.0001060273],"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":[0.001587455,0.0007242971,0.008009875,0.0004228883,0.0004307987,0.0005655137,0.0005341936,0.39389,0.03058262,0.02382808,0.0202075,0.5192167],"study_design_scores_gemma":[0.00003865858,0.0000686875,0.0002758172,0.000004617258,0.00003579374,0.00004312775,0.00001178501,0.9948995,0.001450813,0.001638967,0.001516316,0.00001597799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0448338,0.000477877,0.9438049,0.0003406998,0.00009079918,0.0001995076,0.0006651242,0.007124545,0.002462741],"genre_scores_gemma":[0.5754474,0.0007097786,0.4107853,0.0002277346,0.0001382864,0.0002816679,0.001904108,0.0001878165,0.0103178],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02641611,"threshold_uncertainty_score":0.05252469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04898726975611525,"score_gpt":0.283325210621038,"score_spread":0.2343379408649228,"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."}}