{"id":"W2138759931","doi":"10.1145/2488388.2488445","title":"HeteroMF","year":2013,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Recommender system; Context (archaeology); Matrix decomposition; Cold start (automotive); Factor (programming language); Process (computing); Artificial intelligence; Factor analysis; Information retrieval; Machine learning","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":[0.00004082277,0.00003271166,0.00003981289,0.00002123272,0.0000194093,0.0001306024,0.0003164959,0.00001473709,0.0001333309],"category_scores_gemma":[0.000001173997,0.00002273101,0.00001801558,0.00005129302,0.00000320866,0.000360261,0.00008670129,0.00002063025,0.0005235814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004630327,"about_ca_system_score_gemma":0.000002914458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002316672,"about_ca_topic_score_gemma":0.000002156576,"domain_scores_codex":[0.9997008,0.00001011468,0.0000635321,0.00009355033,0.00005002811,0.00008195497],"domain_scores_gemma":[0.999677,0.00000824077,0.00001182959,0.0002554832,0.0000169001,0.00003051699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[3.99611e-8,0.00002180542,0.000900717,0.000004087662,0.000005553875,0.000001509398,0.0001184837,1.274426e-7,0.001142714,0.4544967,0.2960643,0.247244],"study_design_scores_gemma":[0.0003655687,0.0002763233,0.02838082,0.0000286108,0.000001448419,0.0001078567,0.00004838684,0.05724207,0.07831897,0.2489873,0.5854702,0.0007724746],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002874141,0.00001263007,0.8473201,0.001910371,0.0001254071,0.00008446641,1.937893e-8,0.0004389426,0.1472339],"genre_scores_gemma":[0.9022844,0.000001430601,0.09466223,0.0006566467,0.00001717817,0.00002797217,6.471019e-8,0.000001691029,0.002348351],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8994103,"threshold_uncertainty_score":0.6729755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01028856897737597,"score_gpt":0.2052015021096575,"score_spread":0.1949129331322816,"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."}}