{"id":"W3208350171","doi":"10.1109/access.2021.3122098","title":"Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Ryerson University","keywords":"Computer science; Context (archaeology); Internet of Things; Recommender system; Context model; Context awareness; Ubiquitous computing; Data science; World Wide Web; Human–computer interaction; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.000427762,0.0001834923,0.0002969777,0.00007785492,0.0001231923,0.0006494541,0.001330752,0.00008719235,0.00003523438],"category_scores_gemma":[0.000006873495,0.0001397633,0.0000802034,0.0003317323,0.00002182513,0.0003845477,0.000299422,0.0002086293,0.00003630344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001151033,"about_ca_system_score_gemma":0.00004459213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007631114,"about_ca_topic_score_gemma":0.0001072307,"domain_scores_codex":[0.9979752,0.0005843269,0.0004353548,0.0004572302,0.0002874308,0.0002604506],"domain_scores_gemma":[0.9986749,0.000224483,0.0001877776,0.0007945339,0.00007006573,0.00004819873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001461043,0.0009319936,0.01251623,0.0008184322,0.0002398547,0.0005691968,0.006247883,0.001074975,0.001678248,0.05089812,0.1350095,0.7900009],"study_design_scores_gemma":[0.0010929,0.0001265123,0.02203544,0.0005897454,0.00002292536,0.0003033968,0.001426414,0.02655838,0.007909837,0.002779534,0.9363987,0.0007562078],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06353542,0.01338898,0.8875354,0.0207696,0.006347107,0.002725039,0.00004728266,0.0005305888,0.005120581],"genre_scores_gemma":[0.9959648,0.000519898,0.0005279942,0.002579794,0.0001263639,0.0001836956,0.00001337807,0.0000125171,0.00007160599],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9324293,"threshold_uncertainty_score":0.6262701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1227455152735246,"score_gpt":0.335108337076764,"score_spread":0.2123628218032394,"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."}}