{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008628453,0.0001448077,0.0001859524,0.0002832765,0.000256473,0.0002131422,0.0005760333,0.00009425872,0.00001856026],"category_scores_gemma":[0.0000256079,0.0001215589,0.00009777673,0.0006105128,0.00002078658,0.0004244777,0.0002816825,0.0003092824,0.00002812663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004801929,"about_ca_system_score_gemma":0.00003806889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003466829,"about_ca_topic_score_gemma":0.00002832034,"domain_scores_codex":[0.9984928,0.0001491005,0.0002684359,0.0004551609,0.0002104946,0.000423989],"domain_scores_gemma":[0.9991491,0.0002812982,0.00005145232,0.0003298756,0.00008135229,0.0001068581],"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":[0.00001084389,0.00002609242,0.0001021666,0.00004599869,0.00001756941,0.000002855637,0.00189636,0.003229853,0.0001807442,0.1711662,0.003770576,0.8195508],"study_design_scores_gemma":[0.0003009455,0.0001571667,0.0000815404,0.00002472987,0.000001771682,0.000009156155,0.0001067307,0.8660945,0.001358066,0.07246885,0.05918824,0.0002083248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001456499,0.00001902762,0.9761044,0.006633847,0.0002059483,0.0004624906,0.000002294165,0.001462636,0.01365282],"genre_scores_gemma":[0.761839,0.00008060419,0.2350542,0.0004487415,0.000162235,0.0002953324,0.00005373679,0.00003192167,0.002034194],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8628646,"threshold_uncertainty_score":0.495703,"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."}}