{"id":"W2951780535","doi":"10.48550/arxiv.1809.07428","title":"Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Ranking (information retrieval); Recommender system; Distillation; Computer science; Machine learning; Information retrieval; Chromatography; Chemistry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007412176,0.0004571387,0.0006162339,0.0002989417,0.0006045041,0.000340345,0.001406762,0.0002875155,0.000005593279],"category_scores_gemma":[0.000007146642,0.0004410317,0.0001822166,0.0003995022,0.00006868521,0.0008553418,0.000696288,0.0005480245,0.000008039369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004763782,"about_ca_system_score_gemma":0.0001321871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002215612,"about_ca_topic_score_gemma":0.00001618726,"domain_scores_codex":[0.9975922,0.0002139984,0.0003552387,0.001195354,0.0001563403,0.0004868434],"domain_scores_gemma":[0.9976597,0.0001761323,0.0006336887,0.001051093,0.0003538897,0.0001255027],"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":[0.0001533919,0.00004893491,0.006882834,0.001153741,0.0003253145,0.00004564275,0.0008041374,0.7317597,0.000005195556,0.2560342,0.0006845823,0.002102335],"study_design_scores_gemma":[0.0007647388,0.0001919081,0.0003654238,0.001087536,0.00006671587,0.00002247279,0.0001010032,0.988564,0.0001233297,0.007219186,0.0008956685,0.0005980453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08067598,0.00003791991,0.912209,0.00008204284,0.0006484239,0.0007960462,0.000008033402,0.0009277397,0.00461483],"genre_scores_gemma":[0.9858208,0.00004653142,0.01340517,0.00002985721,0.0002206399,0.000007052384,0.00002999494,0.00004481592,0.0003951087],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9051449,"threshold_uncertainty_score":0.9998041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09029474052494575,"score_gpt":0.193935070073426,"score_spread":0.1036403295484802,"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."}}