{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0006792418,0.0002850147,0.0004267622,0.0002883797,0.002260405,0.000316414,0.0008248293,0.0001053767,0.00003021234],"category_scores_gemma":[0.000001323735,0.0003123635,0.0002628162,0.000669753,0.00003375321,0.0003685131,0.00001937115,0.0004721645,0.00006230817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009470501,"about_ca_system_score_gemma":0.0001771345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001017645,"about_ca_topic_score_gemma":0.00001507309,"domain_scores_codex":[0.9967521,0.0007000293,0.0006576161,0.0005961675,0.0008876261,0.0004064635],"domain_scores_gemma":[0.9988015,0.0002722927,0.0002819184,0.0003377593,0.0001880516,0.0001185084],"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":[0.0001148097,0.004757571,0.0001023124,0.0006382033,0.001088253,0.0001446548,0.01029901,0.6174232,0.0001252069,0.2246899,0.08327392,0.057343],"study_design_scores_gemma":[0.002736424,0.0003292355,0.00007690735,0.00008648638,0.0000333299,0.0003072231,0.002806277,0.9607245,0.0001390879,0.0009400293,0.03102345,0.0007969978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006850656,0.00007068551,0.9903869,0.001108369,0.004182777,0.0007542891,0.0001269535,0.001105739,0.001579239],"genre_scores_gemma":[0.995269,0.000001477073,0.002722809,0.0003330992,0.0001756733,0.0009483877,0.00001438697,0.00003831793,0.0004968436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.994584,"threshold_uncertainty_score":0.9999328,"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."}}