{"id":"W1616993132","doi":"10.1109/tkde.2015.2448541","title":"A Family of Rank Similarity Measures Based on Maximized Effectiveness Difference","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Chinese Academy of Sciences; Google","keywords":"Relevance (law); Measure (data warehouse); Metric (unit); Similarity (geometry); Rank (graph theory); Computer science; Similarity measure; Learning to rank; Maximization; Context (archaeology); Ranking (information retrieval); Data mining; Information retrieval; Mathematics; Artificial intelligence; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01175262,0.001922436,0.002419153,0.007800137,0.0008756741,0.003424219,0.001954912,0.001885551,0.001909718],"category_scores_gemma":[0.04941211,0.0004909444,0.001358279,0.004740885,0.002375011,0.006631453,0.002444386,0.002387034,0.0009947456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002282574,"about_ca_system_score_gemma":0.001443717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007498118,"about_ca_topic_score_gemma":0.0007145903,"domain_scores_codex":[0.9854053,0.005767862,0.001128653,0.00178551,0.005479869,0.0004328835],"domain_scores_gemma":[0.970062,0.0175947,0.003431205,0.003874004,0.004456081,0.0005820409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005666599,0.0003691992,0.008560459,0.001089822,0.0005012152,0.0001872863,0.0005544054,0.1567998,0.01269175,0.2554308,0.008735395,0.5545132],"study_design_scores_gemma":[0.0001012715,0.001304071,0.006836107,0.0001801659,0.0002107149,0.001089731,0.0002438156,0.7485064,0.01359999,0.2139874,0.0136837,0.0002566248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0197892,0.001996956,0.9719189,0.0002983161,0.00006867621,0.0002884664,0.000359618,0.0004497322,0.004830109],"genre_scores_gemma":[0.451519,0.001458631,0.5419756,0.0003102521,0.0004023007,0.0009219023,0.0007453058,0.000318267,0.002348707],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01175262,"threshold_uncertainty_score":0.06215453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07556383053037817,"score_gpt":0.2933464697698414,"score_spread":0.2177826392394632,"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."}}