{"id":"W4361855616","doi":"10.1109/tcss.2023.3259983","title":"FDGNN: Feature-Aware Disentangled Graph Neural Network for Recommendation","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Social Systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; State Key Laboratory of Novel Software Technology; National Natural Science Foundation of China","keywords":"Interpretability; Computer science; Artificial intelligence; Machine learning; Feature (linguistics); Graph; Embedding; Theoretical computer science; Data mining","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":[0.0004431538,0.0002237349,0.0003004652,0.0002234588,0.001054809,0.0003122088,0.000373431,0.0001513711,0.0000059761],"category_scores_gemma":[0.000002345606,0.0002269098,0.0002746721,0.001053168,0.00002735263,0.0003622165,0.000004752247,0.0001847957,0.0000335255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001422607,"about_ca_system_score_gemma":0.00005319754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009044698,"about_ca_topic_score_gemma":0.000028157,"domain_scores_codex":[0.9981917,0.0002054604,0.0004058335,0.000450962,0.0003542668,0.0003917546],"domain_scores_gemma":[0.9989427,0.0004120665,0.0001920147,0.0001701494,0.0001904273,0.00009259178],"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":[0.00006924698,0.0002264241,0.00009622126,0.000179345,0.0003021176,0.000008295034,0.001142257,0.6383134,0.00003348035,0.03157984,0.2325719,0.09547745],"study_design_scores_gemma":[0.0007569746,0.0001680604,0.0007210342,0.00005231478,0.0000218773,0.000017076,0.000151027,0.9747896,0.00005147361,0.009410517,0.01345047,0.0004095255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006160347,0.00001685116,0.9877391,0.004357846,0.004837765,0.0009613801,0.000128222,0.001165109,0.0001776632],"genre_scores_gemma":[0.9924779,0.000005690124,0.005281325,0.000243369,0.0006910046,0.0006931368,0.0001545203,0.00003320523,0.0004198308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9918619,"threshold_uncertainty_score":0.9253112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03829558000491538,"score_gpt":0.2955740400721417,"score_spread":0.2572784600672263,"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."}}