{"id":"W3012881846","doi":"10.1145/3366423.3380130","title":"Off-policy Learning in Two-stage Recommender Systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Recommender system; Computer science; Scalability; Latency (audio); Ranking (information retrieval); Matching (statistics); Stage (stratigraphy); Information retrieval; Machine learning; Artificial intelligence; Database; Telecommunications","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":[0.009450215,0.001653852,0.004457892,0.001194177,0.001456781,0.00264459,0.004544256,0.00474604,0.006692635],"category_scores_gemma":[0.02604968,0.002358834,0.001402047,0.001711078,0.002210145,0.004842858,0.003334089,0.004289902,0.001969618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001880243,"about_ca_system_score_gemma":0.002243047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01156965,"about_ca_topic_score_gemma":0.01077533,"domain_scores_codex":[0.9927189,0.003377105,0.0003847117,0.001419491,0.001285109,0.0008147015],"domain_scores_gemma":[0.9719951,0.02307204,0.0007316264,0.001653312,0.00181608,0.0007319191],"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":[0.0007248887,0.0003834814,0.001835266,0.0002753759,0.0002115244,0.000346958,0.0002637388,0.8595557,0.001006915,0.04849623,0.004649356,0.08225068],"study_design_scores_gemma":[0.00005712889,0.00007276188,0.0001207629,0.000009454361,0.00001851407,0.00003677896,0.00000941364,0.9836538,0.0002313824,0.01527774,0.0004931054,0.00001903847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02496407,0.001455874,0.967562,0.0009444482,0.0001650978,0.0001922066,0.0001879433,0.0007407836,0.003787574],"genre_scores_gemma":[0.8245416,0.001282715,0.1519133,0.0009486468,0.0005038689,0.0005017978,0.0006267772,0.0001684035,0.01951288],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01156965,"threshold_uncertainty_score":0.04997808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04758359732466737,"score_gpt":0.3093846935768848,"score_spread":0.2618010962522174,"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."}}