{"id":"W4387849000","doi":"10.1145/3583780.3614853","title":"Dual-Process Graph Neural Network for Diversified Recommendation","year":2023,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Recommender system; Artificial intelligence; Process (computing); Machine learning; Dual (grammatical number); Diversity (politics); Artificial neural network; Boosting (machine learning); Relevance (law); Graph; Theoretical computer science","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":[0.0003940999,0.0000900185,0.0001126723,0.00009709821,0.0001924607,0.0001296946,0.0003351216,0.00004543773,0.00001676843],"category_scores_gemma":[0.00001017842,0.00007698454,0.00006872557,0.0006376605,0.00000772108,0.0004125061,0.0001344028,0.0000466155,0.00002437186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001155552,"about_ca_system_score_gemma":0.00001118093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003710984,"about_ca_topic_score_gemma":0.00001623307,"domain_scores_codex":[0.9991714,0.00003097223,0.0001698019,0.0002683652,0.00009128938,0.0002681435],"domain_scores_gemma":[0.9994897,0.0001080796,0.00006380997,0.0002276871,0.00006150388,0.00004925333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000785397,0.00002326248,0.001780452,0.00004680341,0.00003037984,0.000003610207,0.0004510715,0.000284577,0.00004734658,0.10382,0.7453281,0.1481765],"study_design_scores_gemma":[0.001033125,0.0003823118,0.005046619,0.00004050707,0.00001316694,0.00001596302,0.0003275174,0.6196221,0.002589082,0.2105622,0.1596209,0.000746573],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003946836,0.000009189295,0.9789731,0.007054339,0.00130221,0.0004226794,0.000004238032,0.001864414,0.006422994],"genre_scores_gemma":[0.9579797,0.00002277696,0.03869928,0.001142989,0.0003383848,0.0002092099,0.00006723249,0.00001654448,0.001523846],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9540329,"threshold_uncertainty_score":0.3139338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05483579504535522,"score_gpt":0.3012538669354625,"score_spread":0.2464180718901073,"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."}}