{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002663722,0.0002404314,0.0003160444,0.0001165243,0.0001083507,0.0007373967,0.0007586076,0.0001483219,0.00001784904],"category_scores_gemma":[0.000009750802,0.0002021216,0.0000404912,0.0005076266,0.00001254046,0.0003811,0.0007250716,0.0002528864,0.00000150677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002219993,"about_ca_system_score_gemma":0.00063464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008560754,"about_ca_topic_score_gemma":0.001211766,"domain_scores_codex":[0.9985582,0.0001329022,0.0002655328,0.000596013,0.0002820687,0.0001653174],"domain_scores_gemma":[0.9980431,0.00005899444,0.0002426123,0.0009342067,0.0006716886,0.00004937976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002466689,0.001643232,0.01246889,0.02097441,0.002459771,0.0009908782,0.2587833,0.02424183,0.02350037,0.4112285,0.04794462,0.1955176],"study_design_scores_gemma":[0.002425075,0.0005714383,0.002838249,0.008232006,0.0001379484,0.00009425446,0.005475821,0.5138151,0.3201679,0.006772215,0.1337122,0.005757748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001515721,0.0002143903,0.9853821,0.002845712,0.0006549428,0.0003769148,0.000002471165,0.0006089437,0.008398834],"genre_scores_gemma":[0.4904626,0.000009204657,0.5081567,0.0008758024,0.0000626227,0.0002203827,0.00003597107,0.00001673381,0.0001600195],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4895733,"threshold_uncertainty_score":0.8242281,"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."}}