{"id":"W3115487106","doi":"10.1145/3437963.3441751","title":"Unbiased Learning to Rank in Feeds Recommendation","year":2021,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"National Natural Science Foundation of China","keywords":"Learning to rank; Ranking (information retrieval); Computer science; Context (archaeology); Rank (graph theory); Set (abstract data type); Information retrieval; Recommender system; Mean reciprocal rank; Position (finance); Machine learning; Focus (optics); Product (mathematics); Artificial intelligence; Mathematics","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":[0.009661954,0.0009623739,0.002168738,0.001873776,0.00100223,0.001714697,0.001663791,0.001543986,0.001700603],"category_scores_gemma":[0.04335092,0.0006372372,0.001032325,0.002381367,0.001336962,0.003587376,0.00203772,0.001645683,0.0007714414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009391436,"about_ca_system_score_gemma":0.001707469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003932431,"about_ca_topic_score_gemma":0.00464918,"domain_scores_codex":[0.9915712,0.004403653,0.0004301252,0.001312389,0.001824828,0.00045779],"domain_scores_gemma":[0.9694261,0.02107571,0.002149852,0.004081866,0.002773683,0.000492763],"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":[0.000679108,0.0003519588,0.02648898,0.0004171025,0.00050358,0.0002350196,0.0004140559,0.358067,0.003446091,0.04128328,0.004045606,0.5640683],"study_design_scores_gemma":[0.00004912937,0.0002684437,0.002514764,0.00003676176,0.00007731943,0.0001185747,0.00005109991,0.9590853,0.001896255,0.03408045,0.001775701,0.00004608829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0394937,0.0008390147,0.9577785,0.0002419838,0.00004010825,0.00009142933,0.0001230868,0.0005208853,0.0008713109],"genre_scores_gemma":[0.7580717,0.0009430351,0.2368909,0.0004026441,0.0002748368,0.0002173238,0.0006075989,0.0001180239,0.002473992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009661954,"threshold_uncertainty_score":0.05109793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232428136652147,"score_gpt":0.2752439355285293,"score_spread":0.2520011218633146,"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."}}