{"id":"W2189191503","doi":"10.1109/trustcom-bigdatase-ispa.2015.563","title":"Similarity Measure Based on Low-Rank Approximation for Highly Scalable Recommender Systems","year":2015,"lang":"en","type":"article","venue":"Trust, Security And Privacy In Computing And Communications","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; Recommender system; Scalability; Singular value decomposition; Computer science; Low-rank approximation; Sparse matrix; Similarity (geometry); Matrix decomposition; Computation; Rank (graph theory); Similarity measure; Data mining; Approximation algorithm; Theoretical computer science; Artificial intelligence; Machine learning; Algorithm; Mathematics; Database","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":[0.001908796,0.0007781023,0.001941973,0.001430178,0.0006357391,0.001401991,0.001495815,0.001131884,0.001874289],"category_scores_gemma":[0.009157538,0.0003604909,0.0008516244,0.002137515,0.0006082307,0.00231308,0.0009860266,0.001452741,0.0008869666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009151001,"about_ca_system_score_gemma":0.0009853429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005638587,"about_ca_topic_score_gemma":0.00376876,"domain_scores_codex":[0.9975562,0.0006880826,0.0001669219,0.0004234497,0.001048271,0.0001169697],"domain_scores_gemma":[0.9964538,0.001761576,0.0003148913,0.0005829302,0.0007880192,0.0000987751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002186276,0.000196092,0.001911796,0.0002833088,0.0001876209,0.0001785612,0.0001520131,0.6931357,0.008572802,0.05072933,0.005668066,0.238766],"study_design_scores_gemma":[0.000006803958,0.00003202396,0.0001520656,0.000004000161,0.000007790287,0.00002675679,0.000007800717,0.9936332,0.0004716955,0.005130011,0.0005199747,0.000007864913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007943579,0.0004713491,0.9904149,0.0001006851,0.0000407551,0.00004332099,0.00006029397,0.0002509933,0.0006740497],"genre_scores_gemma":[0.45123,0.001037322,0.5434694,0.0001820258,0.0002402207,0.0002534885,0.0007093847,0.00008443699,0.002793617],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005638587,"threshold_uncertainty_score":0.01121157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07831400695735763,"score_gpt":0.3127641550735404,"score_spread":0.2344501481161828,"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."}}