{"id":"W181694393","doi":"","title":"Battling Predictability and Overconcentration in Recommender Systems.","year":2009,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Recommender system; Predictability; Forcing (mathematics); Set (abstract data type); Process (computing); Focus (optics); Data science; Information retrieval","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":[0.0004945769,0.00007603812,0.000122334,0.00005443114,0.00003449525,0.000161393,0.0001686735,0.00005144031,0.000003363959],"category_scores_gemma":[0.00001302107,0.00006402724,0.0000146166,0.0001256133,0.000006892922,0.0005703329,0.0000387904,0.00007359943,0.000001132442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004953064,"about_ca_system_score_gemma":0.00001531773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001789455,"about_ca_topic_score_gemma":0.00002430346,"domain_scores_codex":[0.9991824,0.00007967349,0.0002426774,0.0002509352,0.00009476682,0.000149563],"domain_scores_gemma":[0.9995906,0.00004534514,0.00004191509,0.0002543653,0.00002172421,0.00004602242],"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":[0.00000719409,0.0002295138,0.07633925,0.00009451112,0.00001047128,0.00001287922,0.001895224,0.00005220783,0.0006822521,0.7179938,0.007729217,0.1949535],"study_design_scores_gemma":[0.001633224,0.0006481227,0.385182,0.0003152709,0.000006134389,0.0001282583,0.0004737285,0.5421069,0.00403105,0.04546284,0.01905536,0.0009571271],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05578816,0.0003557141,0.9194865,0.002998222,0.0003337327,0.0004187211,5.603473e-7,0.0004173652,0.02020101],"genre_scores_gemma":[0.9895874,0.00004770619,0.010025,0.0002625004,0.00002523462,0.000008835702,5.720207e-7,0.000001787678,0.00004101981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9337992,"threshold_uncertainty_score":0.2610955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01209063114901781,"score_gpt":0.2379525744762763,"score_spread":0.2258619433272585,"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."}}