{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005259388,0.0001249857,0.0001809202,0.0001854718,0.00008187962,0.0001375753,0.0005914522,0.00003816447,0.00007058053],"category_scores_gemma":[0.00001976333,0.0001135218,0.00007462647,0.000216978,0.000008123281,0.0006293571,0.0001311534,0.00009141657,0.00005994297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009432648,"about_ca_system_score_gemma":0.00002702583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009027579,"about_ca_topic_score_gemma":0.0002660872,"domain_scores_codex":[0.9988539,0.00005154407,0.0003842159,0.0003319478,0.00008297459,0.0002953739],"domain_scores_gemma":[0.9992098,0.0001482711,0.00007579887,0.0004577689,0.00004579482,0.00006263044],"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":[0.000002521969,0.0001502584,0.0003888118,0.0000182944,0.00001105606,0.00000138033,0.0002805054,0.000007920929,0.00008497065,0.07989627,0.3203979,0.5987601],"study_design_scores_gemma":[0.0005958193,0.00008322386,0.0003101896,0.00004719607,0.000002196398,0.00003007661,0.00004299365,0.06000333,0.006718553,0.01210795,0.919777,0.0002815253],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009222604,0.00004590674,0.9178767,0.06233279,0.0003366787,0.0004960483,0.000003849032,0.0003696615,0.01761612],"genre_scores_gemma":[0.4907962,0.00004709437,0.5037526,0.002225969,0.0001280575,0.0002228392,0.00001032439,0.00001393972,0.002802967],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.599379,"threshold_uncertainty_score":0.4629285,"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."}}