{"id":"W3012393718","doi":"10.9734/ajrcos/2020/v5i230130","title":"Recommending Curated Content Using Implicit Feedback","year":2020,"lang":"en","type":"article","venue":"Asian Journal of Research in Computer Science","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Collaborative filtering; Matrix decomposition; Regularization (linguistics); RSS; Recommender system; Content (measure theory); Similarity (geometry); Sparse matrix; Data mining; Artificial intelligence; Information retrieval; Machine learning; Mathematics; World Wide Web","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":[0.001675772,0.001233522,0.001779656,0.001621791,0.0005361699,0.0008731572,0.001417243,0.001580945,0.001522609],"category_scores_gemma":[0.006296773,0.0005809828,0.0008843173,0.00153491,0.0005203665,0.002280816,0.0007043888,0.0009551047,0.0008731318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007712443,"about_ca_system_score_gemma":0.001102872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01360281,"about_ca_topic_score_gemma":0.02393523,"domain_scores_codex":[0.9984677,0.0003924118,0.0000922181,0.0004714662,0.0004590126,0.0001171402],"domain_scores_gemma":[0.9966936,0.001558507,0.0003450038,0.0003841941,0.0008686407,0.0001500409],"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":[0.0008339181,0.0009324434,0.0107428,0.0006206841,0.0003763164,0.0004437754,0.0004576524,0.4236957,0.02482005,0.00556544,0.01081257,0.5206986],"study_design_scores_gemma":[0.000015228,0.0000920058,0.0006130653,0.000008703954,0.00002936288,0.00005474994,0.00001028638,0.9960657,0.001684826,0.0007678979,0.0006444399,0.00001372279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1376535,0.001399156,0.8533572,0.0004953713,0.0001721818,0.0002071777,0.0004361375,0.002629032,0.003650259],"genre_scores_gemma":[0.8233261,0.0005099149,0.1669832,0.0002151642,0.0002077647,0.00015361,0.0006546925,0.0001221594,0.007827432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01360281,"threshold_uncertainty_score":0.02704722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3437217803356968,"score_gpt":0.4252862883390568,"score_spread":0.08156450800335996,"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."}}