{"id":"W4236502286","doi":"10.32920/ryerson.14657415","title":"Personalised ranking with single source implicit information for recommendation tasks a similarity based Monte Carlo Bayesian Personalised Ranking","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Business process reengineering; Ranking (information retrieval); Recommender system; Learning to rank; Similarity (geometry); Posterior probability; Bayesian probability; Information retrieval; Data mining; Monte Carlo method; Machine learning; Algorithm; Artificial intelligence; Mathematics; Statistics; Engineering","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":[0.002616229,0.0006698045,0.001755849,0.001173545,0.0006219127,0.001547611,0.001857929,0.002052682,0.003348303],"category_scores_gemma":[0.01014323,0.0007740773,0.001089722,0.001652269,0.0007065901,0.001995766,0.0008426709,0.001650554,0.001100176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0012713,"about_ca_system_score_gemma":0.001256093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008585737,"about_ca_topic_score_gemma":0.007295667,"domain_scores_codex":[0.9974856,0.001044733,0.0001283107,0.0004676398,0.000746998,0.0001267155],"domain_scores_gemma":[0.995903,0.002473392,0.0003024079,0.0005238396,0.000698185,0.00009923418],"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":[0.0001838415,0.000157282,0.00157034,0.0001533708,0.0001417315,0.00007167479,0.00009160651,0.8561091,0.002893969,0.01326765,0.0015856,0.1237738],"study_design_scores_gemma":[0.000008430558,0.00003504344,0.0002386379,0.000007256722,0.000009833599,0.00002575068,0.000003844992,0.995685,0.000402285,0.00327093,0.0003020226,0.00001090783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01189178,0.0002352398,0.9865484,0.0001317441,0.00002133751,0.00004410904,0.00006064561,0.0002273706,0.0008393848],"genre_scores_gemma":[0.5166992,0.0004251404,0.4769639,0.0002050329,0.0001419511,0.0002970222,0.0004548,0.00008100695,0.004731876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008585737,"threshold_uncertainty_score":0.01707155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1413567680700145,"score_gpt":0.379622343900828,"score_spread":0.2382655758308135,"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."}}