{"id":"W344871260","doi":"10.1007/s00530-015-0469-2","title":"RecAm: a collaborative context-aware framework for multimedia recommendations in an ambient intelligence environment","year":2015,"lang":"en","type":"article","venue":"Multimedia Systems","topic":"Music and Audio Processing","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Context (archaeology); Process (computing); Multimedia; Context awareness; Bridge (graph theory); Personalization; World Wide Web; Ambient intelligence; Human–computer interaction; Adaptation (eye); Recommender system","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":[],"consensus_categories":[],"category_scores_codex":[0.0009935843,0.0002527372,0.0003760799,0.0001713052,0.0001395684,0.0002759399,0.0007512058,0.0001794813,0.00001446856],"category_scores_gemma":[0.0003774006,0.0002365495,0.00004667723,0.0004676763,0.00009148297,0.0007337751,0.0001520791,0.0002340062,0.0001066842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003024221,"about_ca_system_score_gemma":0.0002361041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001278446,"about_ca_topic_score_gemma":0.00007136188,"domain_scores_codex":[0.9976123,0.0002827425,0.0006157927,0.0006705205,0.0003725876,0.0004460789],"domain_scores_gemma":[0.9979393,0.000598335,0.0002814595,0.0005880744,0.0002291175,0.0003637162],"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":[0.00007985313,0.0007284764,0.004226811,0.0001288243,0.00004911666,0.00002882887,0.08853497,0.003339152,0.000293634,0.003628899,0.008171094,0.8907903],"study_design_scores_gemma":[0.000686885,0.0002146032,0.0001347112,0.0003231157,0.000006869403,0.000004790344,0.007893067,0.9684551,0.001378896,0.001175529,0.01933818,0.0003882047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002391056,0.0005838199,0.9915593,0.001713737,0.002195678,0.001318543,0.00004697442,0.0001249845,0.0000659073],"genre_scores_gemma":[0.538448,0.0000229403,0.459896,0.0003780698,0.0002800589,0.0007743005,0.00004853537,0.00002394459,0.0001281616],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.965116,"threshold_uncertainty_score":0.9646209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08014619878909854,"score_gpt":0.3239170902465489,"score_spread":0.2437708914574503,"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."}}