{"id":"W2767929746","doi":"10.1145/3132847.3133138","title":"An Empirical Study of Embedding Features in Learning to Rank","year":2017,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Toronto Metropolitan University","funders":"","keywords":"Embedding; Computer science; Rank (graph theory); Ranking (information retrieval); Learning to rank; Word embedding; Artificial intelligence; Word (group theory); Information retrieval; Empirical research; Machine learning; Mathematics; Statistics","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":[0.0220316,0.001117304,0.001180457,0.002057259,0.0006762626,0.001802662,0.00111785,0.001358451,0.002897023],"category_scores_gemma":[0.1887635,0.000333471,0.0006080806,0.003702571,0.001885826,0.006321875,0.001030711,0.002682745,0.0007088472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008129142,"about_ca_system_score_gemma":0.0005002906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001685418,"about_ca_topic_score_gemma":0.001416451,"domain_scores_codex":[0.982775,0.01305219,0.0005721698,0.001018308,0.002160978,0.0004212994],"domain_scores_gemma":[0.6737733,0.2960567,0.009979675,0.01254738,0.006336783,0.001306234],"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":[0.002884725,0.002168506,0.2049973,0.001396969,0.001004027,0.0003321114,0.0007755075,0.2125483,0.002761886,0.03571823,0.01121274,0.5241998],"study_design_scores_gemma":[0.0001997855,0.002113668,0.05028744,0.0001544771,0.0002185416,0.0006986771,0.0005229739,0.8973448,0.003807239,0.04048353,0.004025808,0.0001430344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7468668,0.008440137,0.2325896,0.001914224,0.0001780426,0.0002456111,0.001176443,0.0005690297,0.008020057],"genre_scores_gemma":[0.9795832,0.0004039122,0.01847512,0.00007239495,0.00009869431,0.00005049237,0.0006893565,0.00005060237,0.0005762276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0220316,"threshold_uncertainty_score":0.1165156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04669500883657094,"score_gpt":0.3860673460005514,"score_spread":0.3393723371639805,"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."}}