{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001057197,0.00006460118,0.00009200522,0.00003674962,0.00005365423,0.00009024205,0.0001825981,0.00002980723,0.000006925996],"category_scores_gemma":[0.000004004604,0.00005122687,0.0000344678,0.00008364547,0.000004740783,0.0002263462,0.00002184025,0.00002315951,0.000004390498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002243853,"about_ca_system_score_gemma":0.000009198677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001310871,"about_ca_topic_score_gemma":0.00001998446,"domain_scores_codex":[0.9995307,0.00002684865,0.0001108692,0.0001621973,0.00005973453,0.0001096987],"domain_scores_gemma":[0.9996728,0.00003973568,0.00003146153,0.0001700593,0.00005586763,0.00003003642],"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":[0.00001735408,0.0001315949,0.0001371668,0.0001796306,0.00007350266,0.000002027853,0.006873526,0.00007983382,0.05671959,0.4297064,0.008052437,0.498027],"study_design_scores_gemma":[0.0006559198,0.0006043226,0.001624696,0.00005532515,0.000004160979,0.000007878141,0.00005348968,0.8647398,0.0708499,0.01567162,0.04531658,0.0004163017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003311494,0.000008381027,0.9934743,0.001240671,0.000177296,0.0002556311,0.000004970399,0.0002618078,0.001265432],"genre_scores_gemma":[0.8555744,0.000002681191,0.1440051,0.0001801055,0.00001723718,0.00005666704,0.000002263605,0.000004048472,0.000157541],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.86466,"threshold_uncertainty_score":0.2088971,"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."}}