{"id":"W2100359483","doi":"10.1109/icws.2011.61","title":"Collaborative Filtering Based Service Ranking Using Invocation Histories","year":2011,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Collaborative filtering; Computer science; Recommender system; Ranking (information retrieval); Information retrieval; Information overload; Web service; Similarity (geometry); Service (business); Matching (statistics); Quality of service; Data mining; Quality (philosophy); Database; World Wide Web; Artificial intelligence; Computer network","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005062457,0.001521524,0.00298046,0.005446084,0.001731151,0.002932362,0.002548788,0.001422165,0.001924812],"category_scores_gemma":[0.01990178,0.000823963,0.001542801,0.004749879,0.0007535768,0.003550207,0.0009746292,0.001309835,0.001443325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00169252,"about_ca_system_score_gemma":0.002106745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01666286,"about_ca_topic_score_gemma":0.01904078,"domain_scores_codex":[0.9927873,0.001956736,0.0005100777,0.0009514387,0.003404469,0.0003899665],"domain_scores_gemma":[0.9871818,0.005880343,0.001119287,0.002111585,0.003260673,0.0004463208],"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":[0.0009773268,0.0006591843,0.02201558,0.0004719292,0.0007038837,0.0003496624,0.0004156368,0.1629473,0.01618654,0.01722409,0.009987875,0.768061],"study_design_scores_gemma":[0.00006673723,0.0002435665,0.005296311,0.00003660842,0.0002200495,0.0003666951,0.00009081388,0.9674283,0.01197507,0.00920346,0.004943562,0.0001287728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05635739,0.001237977,0.933774,0.0004522063,0.0001504285,0.0003649282,0.0004486344,0.003443244,0.003771284],"genre_scores_gemma":[0.666768,0.000738427,0.3241815,0.0001427512,0.0002949418,0.0002363462,0.001429375,0.0002144078,0.005994158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01666286,"threshold_uncertainty_score":0.03313178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0780366164483595,"score_gpt":0.2589619154536387,"score_spread":0.1809252990052792,"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."}}