{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001867869,0.0001020113,0.0001515408,0.00007686056,0.00007447734,0.0001574928,0.0003742695,0.00004317348,0.00001386045],"category_scores_gemma":[0.0000274212,0.00008439476,0.00004824013,0.0002692187,0.000006864438,0.0002764105,0.0000246032,0.00003428169,0.000005467388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002195179,"about_ca_system_score_gemma":0.00005359166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004897388,"about_ca_topic_score_gemma":0.000002611277,"domain_scores_codex":[0.999277,0.00002506413,0.0001911845,0.0002316036,0.00008978359,0.0001853778],"domain_scores_gemma":[0.9993507,0.0001025004,0.00006895354,0.0003074364,0.0001229755,0.00004741709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002848877,0.0001950235,0.0001133928,0.00004857815,0.00002792897,0.000005403271,0.00105253,0.00009197965,0.1720173,0.4611093,0.03186336,0.3334467],"study_design_scores_gemma":[0.0004315948,0.0004767139,0.0002786551,0.0000275625,0.000001641291,9.066386e-7,0.00002415976,0.01810703,0.9417757,0.008525109,0.03014104,0.0002099203],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001739988,0.00002455581,0.9741308,0.001588837,0.0001027951,0.0003962376,0.000002838196,0.0004230332,0.02159091],"genre_scores_gemma":[0.6261173,0.000001468839,0.3723461,0.001155847,0.00002806904,0.00006376307,0.000001316017,0.00000307782,0.0002830287],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7697584,"threshold_uncertainty_score":0.3441518,"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."}}