{"id":"W74087571","doi":"10.1007/978-3-642-28765-7_33","title":"A Multi-Agent Recommender System","year":2012,"lang":"en","type":"book-chapter","venue":"Advances in intelligent and soft computing","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Recommender system; Collaborative filtering; Computer science; Personalization; Associative property; World Wide Web; Information retrieval; Information filtering system; User satisfaction; Human–computer interaction; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007642811,0.0005045028,0.0006780767,0.0003201152,0.0001649693,0.0001670878,0.0007767742,0.0002856012,0.00001249702],"category_scores_gemma":[0.00001413289,0.0004698533,0.0001511386,0.00007251718,0.00005561403,0.0004926229,0.0007830666,0.0005586153,0.00004611382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002418322,"about_ca_system_score_gemma":0.00002781202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002436983,"about_ca_topic_score_gemma":0.00002745573,"domain_scores_codex":[0.9975157,0.00006251375,0.0008854223,0.0007460847,0.000275037,0.0005152656],"domain_scores_gemma":[0.9984109,0.0002828885,0.0004697706,0.0005990049,0.00008237806,0.0001550958],"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":[0.000002576667,0.00002952871,0.0003870816,0.0003504428,0.00003552935,0.0000284879,0.0009601911,0.00003702974,0.000002088902,0.2904326,0.0002182529,0.7075161],"study_design_scores_gemma":[0.0002709599,0.00007762347,0.00003284852,0.002790595,0.00002165247,0.0001703737,0.0001829792,0.05799035,0.0001100359,0.007364575,0.9298507,0.001137299],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000009392279,0.04524003,0.8671118,0.00007792479,0.001897766,0.0004493947,0.0000026661,0.0004451788,0.08476586],"genre_scores_gemma":[0.4167932,0.02942214,0.4961613,0.0007320467,0.001910595,0.00009411747,0.00003994546,0.0003168994,0.05452977],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9296325,"threshold_uncertainty_score":0.9997753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04031088134978659,"score_gpt":0.2885038837709059,"score_spread":0.2481930024211193,"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."}}