{"id":"W4387846697","doi":"10.1145/3583780.3614925","title":"iHAS: Instance-wise Hierarchical Architecture Search for Deep Learning Recommendation Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Recommender system; Overfitting; Artificial intelligence; Benchmark (surveying); Embedding; Cluster analysis; Machine learning; Collaborative filtering; Selection (genetic algorithm); Data mining; Artificial neural network","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":[0.001439222,0.00153496,0.001701209,0.0009257314,0.0005340368,0.0007820495,0.00281737,0.001760959,0.00351996],"category_scores_gemma":[0.00478663,0.0008805055,0.001002925,0.001003681,0.0006352107,0.001525884,0.001437527,0.002337769,0.001064613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001131311,"about_ca_system_score_gemma":0.001529941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008494037,"about_ca_topic_score_gemma":0.0220864,"domain_scores_codex":[0.9993562,0.0002358352,0.00003788052,0.0001522649,0.0001320834,0.0000856495],"domain_scores_gemma":[0.9987726,0.0006654924,0.00008706962,0.0002065925,0.0001863786,0.00008191408],"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":[0.0001601951,0.0001882314,0.002616121,0.0001120276,0.0001727391,0.00009296952,0.0001004226,0.7634291,0.002415005,0.01085,0.006829128,0.2130341],"study_design_scores_gemma":[0.00001191073,0.00003191622,0.00006052004,0.000003876736,0.000007208992,0.000007696968,0.000005773819,0.9957053,0.0002260541,0.003757155,0.0001790364,0.000003622953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03438818,0.0005745678,0.9597232,0.0003863037,0.00004878514,0.0001253902,0.0002271338,0.002725129,0.001801439],"genre_scores_gemma":[0.5266445,0.0002725578,0.4662924,0.0007018323,0.0001099523,0.0004122183,0.001054998,0.0002466894,0.00426495],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008494037,"threshold_uncertainty_score":0.01688921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05161849447186025,"score_gpt":0.2991101230201119,"score_spread":0.2474916285482517,"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."}}