{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008746639,0.0009181088,0.0009866289,0.001768134,0.0006420938,0.002217301,0.002385323,0.002100069,0.009668623],"category_scores_gemma":[0.004273352,0.0007268359,0.001844609,0.00222592,0.0006800584,0.002490615,0.000988652,0.001638557,0.004148952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001366078,"about_ca_system_score_gemma":0.0008784014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02437397,"about_ca_topic_score_gemma":0.03259616,"domain_scores_codex":[0.9990989,0.0003057589,0.0000588299,0.0002369796,0.0002128023,0.00008671517],"domain_scores_gemma":[0.9987549,0.0005798693,0.0001047942,0.0002190579,0.0002644273,0.00007694951],"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":[0.000490409,0.0002618426,0.002594533,0.0004531017,0.0002453737,0.0006738341,0.0005322877,0.5094399,0.01023236,0.2474901,0.02184736,0.2057388],"study_design_scores_gemma":[0.00004500928,0.00004567206,0.0003389416,0.00002669111,0.00005715093,0.0001288391,0.00002797893,0.9484543,0.0006288881,0.04183654,0.008377929,0.00003194751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005137146,0.0005446702,0.984871,0.0005221429,0.00008035215,0.00009049619,0.0009667201,0.001854369,0.005933099],"genre_scores_gemma":[0.4562177,0.001741332,0.5149959,0.0005182473,0.0002242647,0.0006544722,0.002743827,0.0003966352,0.02250762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02437397,"threshold_uncertainty_score":0.04846424,"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."}}