{"id":"W2069411646","doi":"10.1145/2623330.2623356","title":"Modeling impression discounting in large-scale recommender systems","year":2014,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Recommender system; Computer science; Impression; Discounting; Scale (ratio); World Wide Web; Information retrieval; Human–computer interaction","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":[0.006746897,0.001333486,0.002045587,0.001313702,0.0008819175,0.003066844,0.003236376,0.002521809,0.004086789],"category_scores_gemma":[0.03105642,0.001644165,0.001374951,0.001556362,0.001268043,0.003782145,0.001476493,0.003112565,0.0008116764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002590309,"about_ca_system_score_gemma":0.001249362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03755661,"about_ca_topic_score_gemma":0.02953316,"domain_scores_codex":[0.9976853,0.0008873134,0.0001429147,0.0006085027,0.0004049088,0.0002709688],"domain_scores_gemma":[0.9787411,0.01668703,0.001650407,0.001006547,0.001292914,0.0006221193],"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":[0.000253924,0.0001848434,0.008049869,0.0001723346,0.0002258809,0.0003529513,0.000350289,0.9227321,0.000815702,0.04432076,0.00222165,0.02031969],"study_design_scores_gemma":[0.00001901785,0.00002154941,0.0006578173,0.000007496574,0.00002496076,0.00002673778,0.00001298327,0.9910766,0.00005662562,0.007818594,0.0002644234,0.00001325918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3207136,0.004295209,0.6597288,0.002168549,0.000321849,0.0002579661,0.00127101,0.0007406943,0.01050239],"genre_scores_gemma":[0.9639426,0.001052177,0.02608021,0.0001440319,0.0002074562,0.0001148995,0.0004621338,0.00006648745,0.007930105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03755661,"threshold_uncertainty_score":0.07467604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01855364099485872,"score_gpt":0.2590754352479612,"score_spread":0.2405217942531025,"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."}}