{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01985437,0.00277818,0.007347308,0.002143964,0.003495927,0.004398283,0.00809803,0.006348257,0.01162099],"category_scores_gemma":[0.1604828,0.002381627,0.003336139,0.003714862,0.003750392,0.02133796,0.00576596,0.006952195,0.004422624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003877521,"about_ca_system_score_gemma":0.003418658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004408788,"about_ca_topic_score_gemma":0.004871327,"domain_scores_codex":[0.9649975,0.01587727,0.002622495,0.005855748,0.007708994,0.002938067],"domain_scores_gemma":[0.7242579,0.2196277,0.007368818,0.03283259,0.01159107,0.004321997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004678502,0.001129475,0.01507603,0.002186822,0.000976151,0.001017111,0.001533948,0.5829062,0.02169855,0.1710126,0.01705674,0.1807278],"study_design_scores_gemma":[0.0001598736,0.0003788656,0.001686548,0.00008285391,0.0001632445,0.0004988626,0.0001932673,0.9049987,0.003352754,0.08597004,0.002417082,0.00009793927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07011692,0.002257379,0.9066068,0.006812759,0.0003207316,0.000474276,0.001311004,0.00228212,0.009818085],"genre_scores_gemma":[0.6355398,0.002039726,0.3462535,0.001742876,0.0009258899,0.001116609,0.002489114,0.0006751262,0.009217437],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01985437,"threshold_uncertainty_score":0.1050012,"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."}}