{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01481063,0.001511027,0.002846046,0.002359238,0.003230287,0.004789204,0.002921819,0.003555939,0.00276435],"category_scores_gemma":[0.08866087,0.002136404,0.001546329,0.002853185,0.007157348,0.01259046,0.006414813,0.008651029,0.001122642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002674303,"about_ca_system_score_gemma":0.002434179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00856041,"about_ca_topic_score_gemma":0.01050552,"domain_scores_codex":[0.9881821,0.005081227,0.0006649026,0.002784248,0.00271182,0.0005757798],"domain_scores_gemma":[0.8852327,0.09138633,0.007151713,0.01020377,0.00485159,0.001173868],"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":[0.001195855,0.0002779983,0.0362376,0.001722376,0.001146171,0.0007795808,0.002794347,0.2270732,0.003383794,0.3756047,0.02732421,0.3224602],"study_design_scores_gemma":[0.00009209525,0.000325504,0.005743828,0.0002551617,0.0002183019,0.0004403183,0.0003319754,0.3980881,0.001901041,0.5800737,0.01233513,0.0001948499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03996565,0.02454252,0.9059598,0.01565955,0.0009141638,0.0001420805,0.0005121179,0.001144956,0.01115913],"genre_scores_gemma":[0.8837685,0.01208007,0.08799299,0.004724333,0.003427785,0.0002870294,0.0005639283,0.0003635956,0.006791692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01481063,"threshold_uncertainty_score":0.078327,"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."}}