{"id":"W2184078221","doi":"","title":"Trust-Based Infinitesimals for Enhanced Collaborative Filtering","year":2009,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Collaborative filtering; Computer science; Recommender system; Infinitesimal; Mean absolute error; Natural (archaeology); Artificial intelligence; Human–computer interaction; Machine learning; Mean squared error; Mathematics; Statistics","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":[0.004554654,0.000800124,0.001312463,0.001287288,0.000659586,0.001274722,0.001629034,0.001066659,0.001922202],"category_scores_gemma":[0.02343757,0.0005516509,0.001127373,0.001065374,0.001701992,0.004230314,0.001524707,0.001709139,0.0005661635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001285959,"about_ca_system_score_gemma":0.0007344052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002207004,"about_ca_topic_score_gemma":0.001939991,"domain_scores_codex":[0.9962967,0.001617281,0.0002419019,0.0006475243,0.001037757,0.0001587226],"domain_scores_gemma":[0.9850333,0.01024529,0.001287577,0.001824636,0.001279934,0.0003292183],"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":[0.0003961771,0.0001365586,0.003175668,0.0003806076,0.000176508,0.0004283728,0.0005575336,0.5231821,0.01009026,0.3382002,0.001942611,0.1213335],"study_design_scores_gemma":[0.000009618486,0.00005759633,0.0002101533,0.000008732603,0.00001206457,0.00006364635,0.00001332043,0.9546691,0.0008985471,0.0432674,0.0007712241,0.00001856827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009575374,0.0001607142,0.9891662,0.000123068,0.00003371288,0.00001481759,0.00002026102,0.0001207076,0.0007851038],"genre_scores_gemma":[0.6951913,0.0004742332,0.3010328,0.0001483684,0.0001562185,0.0001017534,0.0001140842,0.000067804,0.00271344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004554654,"threshold_uncertainty_score":0.02408761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01882172028050308,"score_gpt":0.2832484766795547,"score_spread":0.2644267563990517,"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."}}