{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02849873,0.001609569,0.002651118,0.005959001,0.001559224,0.01068999,0.002288364,0.004830737,0.002968891],"category_scores_gemma":[0.07142273,0.0009293252,0.0009897122,0.01231194,0.003113768,0.01685242,0.002560775,0.004502407,0.002540564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00367868,"about_ca_system_score_gemma":0.003465148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009380118,"about_ca_topic_score_gemma":0.005040144,"domain_scores_codex":[0.9680262,0.01681795,0.002305572,0.002404336,0.009931487,0.0005145142],"domain_scores_gemma":[0.9488194,0.03596351,0.00178648,0.003887249,0.008710036,0.0008334347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001112363,0.0001869282,0.00849236,0.002364713,0.0002815752,0.0000749601,0.0006126529,0.02114187,0.000830705,0.2652966,0.03151118,0.6690952],"study_design_scores_gemma":[0.00005082733,0.0004490091,0.0055849,0.002548774,0.0002239912,0.0007127527,0.001762656,0.2773812,0.001993797,0.5200009,0.1890476,0.0002437809],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01150834,0.2172801,0.715306,0.0314768,0.001251293,0.0004966713,0.001102015,0.00111051,0.02046824],"genre_scores_gemma":[0.1656771,0.125286,0.6958362,0.001467435,0.002713549,0.0006512747,0.001589888,0.0002035187,0.006574866],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02849873,"threshold_uncertainty_score":0.1507174,"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."}}