{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005865247,0.001014994,0.0009617515,0.0007381344,0.0003703128,0.0005768907,0.001718356,0.001383181,0.001213778],"category_scores_gemma":[0.002607642,0.0004920869,0.0008358852,0.001166994,0.0004436583,0.001493056,0.0007611071,0.001818348,0.0003953867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001010578,"about_ca_system_score_gemma":0.0007442829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0234915,"about_ca_topic_score_gemma":0.03147684,"domain_scores_codex":[0.9996393,0.00009555266,0.00001900613,0.0001239671,0.00008164732,0.00004066991],"domain_scores_gemma":[0.9993698,0.0003094891,0.00005692054,0.0001048124,0.0001321409,0.00002687362],"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":[0.0001660471,0.0001520401,0.001875973,0.0001235056,0.0001590203,0.0001078749,0.00009445255,0.6879538,0.004851594,0.01197107,0.004986706,0.2875579],"study_design_scores_gemma":[0.000004563238,0.00001294099,0.0001403666,0.000003932242,0.000008494209,0.00000952105,0.000002339194,0.995461,0.0002811834,0.003738215,0.0003326393,0.00000472921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01909532,0.0008011994,0.9771804,0.0002724579,0.00008572967,0.0000439677,0.0002467022,0.001061989,0.001212152],"genre_scores_gemma":[0.6572841,0.0009430647,0.3329976,0.0004669016,0.0001181197,0.0002081439,0.001477224,0.0001515974,0.006353132],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0234915,"threshold_uncertainty_score":0.04670954,"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."}}