{"id":"W1593027538","doi":"10.1109/eee.2005.115","title":"Semantic Feedback for Hybrid Recommendations in Recommendz","year":2005,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Recommender system; Collaborative filtering; Simple (philosophy); Information retrieval; Domain (mathematical analysis); Artificial intelligence; Natural language processing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007101184,0.001049172,0.001659975,0.002174047,0.001270836,0.002427914,0.00175553,0.002103592,0.007378842],"category_scores_gemma":[0.01883999,0.0006372936,0.0008595469,0.00181406,0.0009180323,0.007032224,0.002432011,0.001375273,0.002900261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001188424,"about_ca_system_score_gemma":0.0009570819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00416773,"about_ca_topic_score_gemma":0.007864255,"domain_scores_codex":[0.9945735,0.001939566,0.0003505766,0.0006747906,0.002219062,0.0002425489],"domain_scores_gemma":[0.9912236,0.004918756,0.0004207197,0.001501854,0.001753562,0.0001815723],"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":[0.002019929,0.0008695318,0.004150821,0.0009576259,0.0002655128,0.000307741,0.0007623684,0.1054699,0.02107452,0.0894105,0.01481642,0.7598951],"study_design_scores_gemma":[0.0002171177,0.0004161996,0.001388243,0.00006984394,0.0001344705,0.000258284,0.000148243,0.9060668,0.01509978,0.05728111,0.01882056,0.00009943966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0324598,0.0008154531,0.9570723,0.0004132714,0.0001210284,0.0002434534,0.0003035376,0.003929135,0.004642107],"genre_scores_gemma":[0.4360543,0.0005947849,0.5517243,0.0003010535,0.0002319271,0.0004089049,0.001058399,0.0004789995,0.009147355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007378842,"threshold_uncertainty_score":0.03755504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03001847940834803,"score_gpt":0.2912304771890068,"score_spread":0.2612119977806588,"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."}}