{"id":"W3097103183","doi":"10.3390/app10217748","title":"Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":398,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Toronto Metropolitan University","funders":"Ryerson University","keywords":"Recommender system; Computer science; Information overload; Field (mathematics); Data science; Quality (philosophy); World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008852211,0.0001377765,0.0002086777,0.0001610521,0.0002673698,0.000485263,0.0006807084,0.00005315789,0.00000687558],"category_scores_gemma":[0.00002698634,0.0001131689,0.00001608708,0.0007933715,0.0001150254,0.0006048654,0.000287406,0.00007775507,0.00000894102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001623457,"about_ca_system_score_gemma":0.00006338632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007034769,"about_ca_topic_score_gemma":0.000002323033,"domain_scores_codex":[0.998669,0.0000568373,0.0002574442,0.0005120994,0.0002788134,0.0002257554],"domain_scores_gemma":[0.9993675,0.0001067291,0.0001404616,0.0001746659,0.00007351403,0.0001371287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[7.842743e-7,0.00001258027,0.00003307742,0.00005842308,0.000007520173,0.000002557151,0.0007181368,0.000004088979,0.00005114645,0.552642,0.002864881,0.4436047],"study_design_scores_gemma":[0.0005794372,0.0003857821,0.0008156502,0.00007072648,0.00001703894,0.00006879935,0.01017791,0.1083904,0.001300987,0.01059208,0.8664815,0.00111974],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006018059,0.004345735,0.7964707,0.04403452,0.0007588686,0.0005839309,0.00000579541,0.0008621502,0.1523365],"genre_scores_gemma":[0.9530645,0.003490421,0.04200724,0.001114417,0.0001922628,0.00007852173,0.000003968951,0.000009074667,0.00003958003],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9524627,"threshold_uncertainty_score":0.4679402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.214370222240161,"score_gpt":0.2810348115815397,"score_spread":0.0666645893413787,"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."}}