{"id":"W4246269187","doi":"10.32920/ryerson.14651886.v1","title":"Collaborative filtering based service ranking with invocation histories","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Collaborative filtering; Computer science; Recommender system; Ranking (information retrieval); Information overload; Information retrieval; Similarity (geometry); Service (business); Web service; Matching (statistics); Quality of service; Quality (philosophy); Data mining; Cold start (automotive); World Wide Web; Artificial intelligence; Computer network","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":[0.006202138,0.001586876,0.003271503,0.004358548,0.001895293,0.003035463,0.003100379,0.001823254,0.002415642],"category_scores_gemma":[0.02367459,0.001034101,0.001856363,0.005344744,0.001044484,0.004133209,0.001304143,0.00172042,0.001574492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001980084,"about_ca_system_score_gemma":0.002647719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01606044,"about_ca_topic_score_gemma":0.01648097,"domain_scores_codex":[0.9907794,0.003020119,0.0005975975,0.00132929,0.003752363,0.0005212657],"domain_scores_gemma":[0.9839116,0.008346082,0.001326615,0.002866322,0.003113728,0.0004357347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009286125,0.000578719,0.01347062,0.0004943634,0.0006074407,0.0003320931,0.0004252889,0.3225385,0.01044659,0.03284289,0.008686201,0.6086487],"study_design_scores_gemma":[0.00004519496,0.0001614627,0.001802485,0.00002234556,0.0001178521,0.0002095961,0.00004777073,0.9812713,0.004322735,0.009173433,0.002757251,0.00006851395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02691921,0.0007610464,0.966922,0.0003111163,0.000102315,0.0002657353,0.0002512754,0.001779177,0.00268823],"genre_scores_gemma":[0.5642634,0.0006601964,0.4258867,0.0001611567,0.0003137962,0.0003235601,0.001028779,0.000190262,0.007172117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01606044,"threshold_uncertainty_score":0.03280038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01889068577651941,"score_gpt":0.2410945018808532,"score_spread":0.2222038161043338,"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."}}