{"id":"W3015139571","doi":"10.1145/3341105.3374002","title":"Iterative learning to rank from explicit relevance feedback","year":2020,"lang":"en","type":"article","venue":"","topic":"Expert finding and Q&A systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Mitacs","keywords":"Computer science; Relevance (law); Rank (graph theory); Learning to rank; Information retrieval; Relevance feedback; Infinite impulse response; Quality (philosophy); Machine learning; Artificial intelligence; Ranking (information retrieval); Image retrieval; Filter (signal processing); Mathematics","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":[0.005472894,0.002007387,0.002333564,0.002869247,0.0008019365,0.001860676,0.002507558,0.001998195,0.003546124],"category_scores_gemma":[0.02604275,0.0006067719,0.001049145,0.002457343,0.001196537,0.003152371,0.001790681,0.002568404,0.002237962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001141825,"about_ca_system_score_gemma":0.002558459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004086631,"about_ca_topic_score_gemma":0.00746219,"domain_scores_codex":[0.9945591,0.002502101,0.0003633751,0.0007582653,0.001467123,0.0003500749],"domain_scores_gemma":[0.981223,0.01187001,0.001282826,0.002026223,0.003214334,0.000383606],"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":[0.0005160157,0.0006951142,0.002725295,0.0004713266,0.0002022548,0.0001131696,0.0002665476,0.2124846,0.006841138,0.01102431,0.01384234,0.750818],"study_design_scores_gemma":[0.00006677944,0.000274914,0.0003633817,0.0000267888,0.00004034725,0.0000814796,0.00002614664,0.9821094,0.003935308,0.01138732,0.001654745,0.00003336987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01417373,0.0008420466,0.9807028,0.0003025961,0.00008157773,0.0002138502,0.00017223,0.001691738,0.001819407],"genre_scores_gemma":[0.4714699,0.0007429035,0.5159366,0.0005021509,0.0006239385,0.0006235105,0.001341582,0.0002983169,0.008461132],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005472894,"threshold_uncertainty_score":0.02894378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02671474515489792,"score_gpt":0.2481323306937224,"score_spread":0.2214175855388245,"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."}}