{"id":"W1967848549","doi":"10.3745/jips.2012.8.2.191","title":"An Adaptive Approach to Learning the Preferences of Users in a Social Network Using Weak Estimators","year":2012,"lang":"en","type":"article","venue":"Journal of Information Processing Systems","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Estimator; Computer science; Stationary distribution; Range (aeronautics); Recommender system; Distribution (mathematics); Tracking (education); Mathematical optimization; Artificial intelligence; Machine learning; 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":[],"consensus_categories":[],"category_scores_codex":[0.002443024,0.00009611363,0.0002211706,0.000260371,0.000177681,0.0003351734,0.000729556,0.00005838571,1.779131e-7],"category_scores_gemma":[0.00009741372,0.00006548387,0.00003084334,0.0006773326,0.0000318483,0.007060419,0.0000780329,0.0002297062,6.903204e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009020214,"about_ca_system_score_gemma":0.0001807914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003424435,"about_ca_topic_score_gemma":4.768157e-7,"domain_scores_codex":[0.9983697,0.0001704196,0.0007647879,0.00005335546,0.0004269147,0.0002148108],"domain_scores_gemma":[0.9980855,0.00004673945,0.001399629,0.0001160968,0.0002875155,0.00006451821],"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":[0.0001522945,0.0002655728,0.05273193,0.0007033864,0.00008385217,8.621171e-7,0.1815865,0.5769119,0.0002577357,0.03025597,0.002393721,0.1546562],"study_design_scores_gemma":[0.000259378,0.0003068483,0.006796683,0.001086291,0.00001891887,0.0001701915,0.01428611,0.9753724,0.000184637,0.0002825141,0.001006948,0.0002290978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1104593,0.000109756,0.8880426,0.00001915819,0.0001760916,0.0001420986,8.27791e-7,0.00003588209,0.0010142],"genre_scores_gemma":[0.8465101,0.00000105243,0.1533332,0.00001515157,0.0001301763,0.000004627983,9.583728e-7,0.000003452405,0.000001305443],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7360508,"threshold_uncertainty_score":0.5118634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04396156569965768,"score_gpt":0.2987358727229033,"score_spread":0.2547743070232456,"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."}}