{"id":"W4319587114","doi":"10.1109/dsaa54385.2022.10032442","title":"Incorporating Accuracy and Diversity in a News Recommender System","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Chiropractic Association; University of Toronto","funders":"","keywords":"Recommender system; Computer science; Representation (politics); Variety (cybernetics); Diversity (politics); Information retrieval; Architecture; Reading (process); Tower; Function (biology); Artificial intelligence; World Wide Web; Engineering","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":[0.004445416,0.0009446866,0.001671557,0.001578646,0.0009906419,0.001881552,0.001864647,0.002152737,0.001131225],"category_scores_gemma":[0.01514823,0.0006421051,0.000614166,0.001122512,0.0006844694,0.004146033,0.002050892,0.002423512,0.0007930539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00116736,"about_ca_system_score_gemma":0.001060758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009856801,"about_ca_topic_score_gemma":0.01576542,"domain_scores_codex":[0.9975062,0.0006026946,0.000204357,0.0005916482,0.0008448879,0.0002502498],"domain_scores_gemma":[0.9938383,0.002861528,0.0003581588,0.0007405697,0.001958287,0.0002432549],"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":[0.0007922507,0.0005266406,0.02944608,0.0002176663,0.0004978355,0.0002761265,0.0005585893,0.4256645,0.01250336,0.006049472,0.00652233,0.5169451],"study_design_scores_gemma":[0.00002552933,0.0001005475,0.001956476,0.0000158391,0.00006360748,0.00006414892,0.00003390199,0.9918218,0.001744981,0.003348645,0.0007957359,0.00002876165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2666061,0.00266445,0.7168974,0.002173615,0.0002560674,0.0001428883,0.0005455448,0.002220744,0.008493243],"genre_scores_gemma":[0.9217573,0.0003050364,0.07382104,0.0003200472,0.0001872937,0.00005321854,0.0003534604,0.00005476924,0.00314788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009856801,"threshold_uncertainty_score":0.02350992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1428890964832219,"score_gpt":0.3463160925396421,"score_spread":0.2034269960564201,"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."}}