{"id":"W4312547900","doi":"10.1109/tcss.2022.3223516","title":"MCARS-CC: A Salable Multicontext-Aware Recommender System","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Social Systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Mean squared error; Leverage (statistics); Recommender system; Scalability; Data mining; Context (archaeology); Machine learning; Mean absolute error; Cluster analysis; Benchmark (surveying); Artificial intelligence; Statistics; Database; Mathematics","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":[0.001096703,0.001107034,0.001579138,0.001729995,0.001151624,0.0009273267,0.002755035,0.001483426,0.002278183],"category_scores_gemma":[0.00309646,0.0005590194,0.001269701,0.002052,0.0002881031,0.001634221,0.001345515,0.001189673,0.002246259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007676646,"about_ca_system_score_gemma":0.001535847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04707906,"about_ca_topic_score_gemma":0.08932716,"domain_scores_codex":[0.998717,0.000281982,0.00007968905,0.0004444718,0.0003803366,0.00009658464],"domain_scores_gemma":[0.9984025,0.0003636066,0.00009957513,0.0004193389,0.000619377,0.00009548688],"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":[0.0007874179,0.0005143276,0.009763106,0.0006015606,0.0008978703,0.0005861177,0.000456485,0.1880562,0.0189469,0.007983609,0.04577912,0.7256273],"study_design_scores_gemma":[0.00005273855,0.0001698767,0.001914194,0.00002584153,0.0001336717,0.0002369486,0.00009283706,0.9786483,0.003424239,0.00313083,0.01210214,0.00006847961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04073458,0.002955201,0.9379814,0.0006157738,0.0004278655,0.0003999056,0.001655596,0.009501133,0.005728578],"genre_scores_gemma":[0.4476788,0.001287248,0.5323867,0.0006191207,0.0002534353,0.0003144243,0.003640723,0.0003031928,0.01351644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04707906,"threshold_uncertainty_score":0.09361005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03015307931476022,"score_gpt":0.2590607501510893,"score_spread":0.228907670836329,"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."}}