{"id":"W2402457424","doi":"10.1137/1.9781611973440.52","title":"Latent Factor Transition for Dynamic Collaborative Filtering","year":2014,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation Singapore","keywords":"Computer science; Collaborative filtering; Recommender system; Matrix decomposition; Scalability; Factor (programming language); Probabilistic logic; Bayesian probability; Stochastic matrix; Machine learning; Artificial intelligence; Data mining; Theoretical computer science; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004667164,0.00106466,0.001569066,0.001360938,0.0008553182,0.001303097,0.001936464,0.001724583,0.003573922],"category_scores_gemma":[0.01728373,0.0008231837,0.001661399,0.001993624,0.001440028,0.003477137,0.001394313,0.002855864,0.001541959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001797516,"about_ca_system_score_gemma":0.00148855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01827576,"about_ca_topic_score_gemma":0.0145297,"domain_scores_codex":[0.9971594,0.001151101,0.0001221286,0.000841303,0.000501877,0.0002242917],"domain_scores_gemma":[0.9919836,0.005743197,0.0005230065,0.0009092623,0.0006749956,0.000165884],"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":[0.0003129727,0.0001886955,0.00358337,0.0002198651,0.0002324075,0.000139939,0.0004713575,0.6735414,0.00230347,0.1501987,0.004239392,0.1645684],"study_design_scores_gemma":[0.00001134512,0.0000217275,0.0003300605,0.00001309919,0.00001560679,0.00002835959,0.00001202051,0.9578498,0.0002642731,0.04042323,0.001011444,0.00001909312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003839271,0.0002287965,0.995011,0.0001292467,0.00002419136,0.00002468807,0.0001041682,0.000236225,0.0004025116],"genre_scores_gemma":[0.5140911,0.00121402,0.4766304,0.0003178906,0.0002298325,0.0004585617,0.001432964,0.0001948228,0.005430499],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01827576,"threshold_uncertainty_score":0.03633881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.014917784051941,"score_gpt":0.256420791838912,"score_spread":0.241503007786971,"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."}}