{"id":"W2140834120","doi":"10.1109/tmm.2007.911226","title":"A Graphical Model for Context-Aware Visual Content Recommendation","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Recommender system; Information overload; Information retrieval; Context (archaeology); World Wide Web; The Internet; Digital library; Human–computer interaction; Multimedia","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":[0.0004323022,0.0001520126,0.0001516653,0.0002168423,0.0002040392,0.00005830036,0.000279028,0.0001318204,0.00001972324],"category_scores_gemma":[0.00001271602,0.0001412076,0.0001586114,0.0002839722,0.000064467,0.0003434037,0.00000165747,0.0002064741,0.00002618857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007837891,"about_ca_system_score_gemma":0.00004946243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001256387,"about_ca_topic_score_gemma":0.00004202985,"domain_scores_codex":[0.9988177,0.00002862169,0.0003292063,0.0003523224,0.0001961318,0.0002759713],"domain_scores_gemma":[0.9990203,0.0003003308,0.00008455689,0.0002271951,0.0002285561,0.0001390448],"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":[0.0001187304,0.0004159164,0.00000566074,0.00001098846,0.00002530328,0.000001148392,0.0004167555,0.00006262546,0.01340276,0.0008541929,0.0002302321,0.9844557],"study_design_scores_gemma":[0.0005928024,0.0001433673,0.00007052718,0.00001067493,0.00001001215,0.000003191374,0.00005183953,0.7981567,0.199984,0.0003008459,0.0005247106,0.0001513575],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005340591,0.000006528038,0.9966784,0.001324259,0.0004302482,0.0005055909,0.00002931213,0.0004370286,0.00005460754],"genre_scores_gemma":[0.9248341,0.00001630711,0.07383353,0.0006253756,0.00003359431,0.0001205819,0.00001060954,0.00001375598,0.0005121911],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9843043,"threshold_uncertainty_score":0.5758281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06997677448712307,"score_gpt":0.3230960029260488,"score_spread":0.2531192284389258,"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."}}