{"id":"W3124075878","doi":"","title":"Improving Social Recommender Systems","year":2008,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Recommender system; Information overload; Computer science; World Wide Web; Internet privacy; Information retrieval; Data science","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":[0.008772203,0.001653317,0.002094023,0.002693865,0.001418709,0.002758678,0.00218677,0.002925007,0.00847411],"category_scores_gemma":[0.050516,0.0007357684,0.001179013,0.002981232,0.000502563,0.005847848,0.002455958,0.002196671,0.004558647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001142361,"about_ca_system_score_gemma":0.001528792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006271852,"about_ca_topic_score_gemma":0.009242208,"domain_scores_codex":[0.9901039,0.004616218,0.0004711838,0.00124577,0.003171532,0.0003913724],"domain_scores_gemma":[0.9737625,0.01395505,0.001053179,0.004634409,0.006049694,0.0005452441],"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":[0.0004499963,0.0008982332,0.01460334,0.001524571,0.0008193823,0.000192958,0.0006216358,0.07438924,0.01218574,0.02337799,0.04115977,0.8297771],"study_design_scores_gemma":[0.0004462565,0.001672547,0.01465052,0.0004634467,0.001459416,0.000675265,0.0007188334,0.781967,0.01501658,0.07143364,0.1112631,0.0002333284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1187119,0.02151009,0.8078039,0.007076328,0.002239044,0.0009156161,0.001355845,0.005860242,0.03452707],"genre_scores_gemma":[0.5177203,0.00904224,0.4537953,0.00123146,0.001845918,0.0003889592,0.001583319,0.0002999478,0.01409253],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008772203,"threshold_uncertainty_score":0.04639244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01976591005859053,"score_gpt":0.237571945015688,"score_spread":0.2178060349570975,"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."}}