{"id":"W2161308157","doi":"10.1109/ciss.2008.4558538","title":"A lower-bound on the number of rankings required in recommender systems using collaborativ filtering","year":2008,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Recommender system; Collaborative filtering; Computer science; Ranking (information retrieval); Class (philosophy); Information retrieval; Graph; Rank (graph theory); Set (abstract data type); Machine learning; Theoretical computer science; Artificial intelligence; Mathematics; Combinatorics","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":[0.000647881,0.0001493255,0.0002817168,0.00009899171,0.0001340266,0.000112002,0.0005944034,0.00006974318,0.00002621875],"category_scores_gemma":[0.0000239104,0.0001000875,0.00005298485,0.0006555393,0.0000428017,0.0003627088,0.000160114,0.0001168132,0.000007976759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009992625,"about_ca_system_score_gemma":0.00007020534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001055724,"about_ca_topic_score_gemma":0.0000262214,"domain_scores_codex":[0.9985633,0.0002010818,0.0004627842,0.0002806549,0.0002564532,0.0002356834],"domain_scores_gemma":[0.9989114,0.000177279,0.0001852476,0.000600968,0.00009139323,0.00003364231],"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":[0.0001244941,0.0009722532,0.03926048,0.0003615239,0.0002513613,0.0002288018,0.01642519,0.0007054438,0.02612312,0.8533528,0.05773985,0.004454704],"study_design_scores_gemma":[0.007073679,0.001437818,0.01015888,0.00583589,0.0000374158,0.00263964,0.004001552,0.6972136,0.1467351,0.02182068,0.09850551,0.004540192],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6540276,0.00007273749,0.3062233,0.001464496,0.0009893718,0.0007481602,0.000002627186,0.0002321023,0.03623958],"genre_scores_gemma":[0.9881386,0.0000171243,0.01123422,0.0002263976,0.0000357705,0.00003332904,2.846242e-7,0.0000113628,0.0003029695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8315321,"threshold_uncertainty_score":0.4081449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08329468142238905,"score_gpt":0.3001969966255199,"score_spread":0.2169023152031309,"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."}}