{"id":"W4391637179","doi":"10.32920/25191023.v1","title":"News Recommender System Considering Temporal Dynamics and Accuracy-Diversity Tradeoff","year":2024,"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":"Recommender system; Computer science; Context (archaeology); Relevance (law); Diversity (politics); Focus (optics); Term (time); Information retrieval; Collaborative filtering; Dynamics (music); Data science; World Wide Web; Psychology; Political science","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":[0.00222367,0.001050318,0.002362387,0.0007695015,0.001399165,0.002043855,0.002501992,0.002183656,0.002582017],"category_scores_gemma":[0.007227342,0.0009398162,0.001057648,0.001348969,0.0006014337,0.002996421,0.001114439,0.001872713,0.001205571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001108659,"about_ca_system_score_gemma":0.00151142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0249407,"about_ca_topic_score_gemma":0.0220378,"domain_scores_codex":[0.9979961,0.000386982,0.0001463432,0.0007293933,0.0004397246,0.0003014931],"domain_scores_gemma":[0.9950563,0.002459155,0.0004216385,0.0005232544,0.001294445,0.0002450712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000726985,0.0003319824,0.01076812,0.0004985095,0.0003996712,0.001132068,0.0006255931,0.7272739,0.02001161,0.02965696,0.0155799,0.1929946],"study_design_scores_gemma":[0.00001656668,0.00004461769,0.0003826315,0.00000585981,0.00003241007,0.00009965663,0.00002370554,0.996559,0.0004858722,0.001634714,0.0006986859,0.00001621395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08076368,0.002225248,0.9071203,0.00146976,0.0002639356,0.000115434,0.0004732331,0.000975868,0.006592533],"genre_scores_gemma":[0.8543171,0.001243382,0.1291964,0.0003014129,0.0004050634,0.0001356269,0.000618838,0.0001034693,0.01367873],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0249407,"threshold_uncertainty_score":0.04959106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04705088236031008,"score_gpt":0.2674393548669578,"score_spread":0.2203884725066477,"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."}}