{"id":"W2952451690","doi":"10.48550/arxiv.1301.2309","title":"Symmetric Collaborative Filtering Using the Noisy Sensor Model","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Collaborative filtering; Similarity (geometry); Computer science; Preference; Recommender system; Process (computing); The Internet; Information retrieval; Bayes' theorem; Machine learning; Artificial intelligence; Data mining; Mathematics; Bayesian probability; World Wide Web; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002901761,0.0003386785,0.0003629392,0.0003595829,0.0002805739,0.0003956234,0.002086641,0.0002547903,0.000008142532],"category_scores_gemma":[0.00001953475,0.000287266,0.0001722876,0.001197789,0.00007097759,0.0004986417,0.002687623,0.0005316742,0.0000244206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002808269,"about_ca_system_score_gemma":0.0002203306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005339599,"about_ca_topic_score_gemma":0.00001560553,"domain_scores_codex":[0.9981843,0.0002128915,0.000238032,0.0008953068,0.0001128618,0.0003565913],"domain_scores_gemma":[0.9976314,0.0001125975,0.0003553975,0.001476177,0.000311348,0.0001130306],"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":[0.000007718451,0.00007736102,0.0004643489,0.0001271373,0.0002148321,0.000140719,0.00119382,0.6423229,0.0004819205,0.3517371,0.002227611,0.001004503],"study_design_scores_gemma":[0.0001155396,0.00001901514,0.00004331037,0.00008111767,0.00002851268,0.000007219428,0.0001353157,0.9758041,0.0005359146,0.02250371,0.0003702099,0.0003560426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1016727,0.0000924143,0.892982,0.0001212749,0.0004639172,0.0005654882,0.00001441452,0.0003124671,0.00377529],"genre_scores_gemma":[0.9671615,0.0001233471,0.03150767,0.0001055269,0.00007278172,0.000003607647,0.000002528778,0.00002112128,0.001001857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8654888,"threshold_uncertainty_score":0.999958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1262993716731614,"score_gpt":0.2111513909393744,"score_spread":0.08485201926621294,"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."}}