{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002155257,0.0001839561,0.0002980513,0.0001684198,0.0004276075,0.0003657043,0.001030628,0.0001397545,2.83165e-7],"category_scores_gemma":[0.0001385038,0.0001791416,0.00004423162,0.0002898539,0.00006578491,0.0002891056,0.0004958953,0.0003327931,9.742179e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007104238,"about_ca_system_score_gemma":0.00007646489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003242836,"about_ca_topic_score_gemma":0.00002427153,"domain_scores_codex":[0.9982852,0.0004907903,0.0004277003,0.0003610598,0.0001833612,0.0002518965],"domain_scores_gemma":[0.9974514,0.0006859408,0.0001764708,0.001387734,0.0001636824,0.0001347971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001562986,0.00209616,0.01422314,0.001166033,0.0001084189,0.000002911734,0.02995385,0.002294715,0.00004640236,0.8169879,0.01495195,0.1180123],"study_design_scores_gemma":[0.0009320542,0.0001483208,0.0004478725,0.0002643111,0.000007462568,0.000006878112,0.0002720559,0.9667645,0.00006575906,0.01728776,0.01357216,0.0002309072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03293982,0.002466694,0.9380915,0.01813286,0.0004146911,0.001673224,0.00002676829,0.0005932755,0.005661133],"genre_scores_gemma":[0.9489473,0.000091373,0.05043388,0.0003624495,0.00003802817,0.0000806408,0.00002677928,0.00001070404,0.000008822759],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9644697,"threshold_uncertainty_score":0.7305183,"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."}}