{"id":"W1979148915","doi":"10.1002/sam.11184","title":"Content‐boosted matrix factorization techniques for recommender systems","year":2013,"lang":"en","type":"article","venue":"Statistical Analysis and Data Mining The ASA Data Science Journal","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Recommender system; Matrix decomposition; Collaborative filtering; Matrix (chemical analysis); Factorization; Non-negative matrix factorization","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":[0.004077209,0.001419696,0.00168879,0.001942971,0.0008647368,0.001294896,0.001677089,0.001893409,0.004750344],"category_scores_gemma":[0.01608636,0.000862319,0.001539275,0.00249629,0.0008884579,0.00254389,0.001131249,0.003140523,0.002157052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009879312,"about_ca_system_score_gemma":0.0009238577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007765443,"about_ca_topic_score_gemma":0.008585968,"domain_scores_codex":[0.9965988,0.001704314,0.0001350395,0.0005309918,0.0008933729,0.000137426],"domain_scores_gemma":[0.9900526,0.006794681,0.0005306829,0.0008844348,0.001593321,0.0001443509],"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":[0.0002602563,0.000339682,0.002049008,0.0009524212,0.0005558149,0.0002155297,0.0004131551,0.4214579,0.009498687,0.1007238,0.01993472,0.4435991],"study_design_scores_gemma":[0.00002750917,0.00006111778,0.0002472015,0.00003994076,0.00004140369,0.00005242458,0.00001947006,0.9489918,0.00105545,0.04614091,0.003299821,0.00002291707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003628721,0.001564314,0.9927235,0.0004465835,0.0001147505,0.00006547835,0.0001215553,0.0002342436,0.001100904],"genre_scores_gemma":[0.2332292,0.00263059,0.7573197,0.000424579,0.0006530735,0.000321794,0.0006677139,0.00008768989,0.004665733],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007765443,"threshold_uncertainty_score":0.02156264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1579030355591327,"score_gpt":0.3788280581645315,"score_spread":0.2209250226053988,"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."}}