{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005571826,0.0006244819,0.0007824696,0.0006088858,0.0003373879,0.0006015392,0.001086795,0.00103426,0.001872565],"category_scores_gemma":[0.002251929,0.0003595292,0.0005368912,0.0008412516,0.0004032212,0.0009124111,0.0006809105,0.001422863,0.0004906401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008272117,"about_ca_system_score_gemma":0.0006698853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01158389,"about_ca_topic_score_gemma":0.01509456,"domain_scores_codex":[0.999731,0.00006151467,0.00001338324,0.00008479541,0.00007068374,0.00003871096],"domain_scores_gemma":[0.9994784,0.0002671589,0.00004349639,0.00004711832,0.0001373043,0.00002655126],"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":[0.0001230549,0.0001005536,0.001246942,0.00009681063,0.00008469491,0.0000869242,0.00006580558,0.8176357,0.002842575,0.01048272,0.002786663,0.1644477],"study_design_scores_gemma":[0.000004708678,0.00001265667,0.00009410759,0.000003196207,0.000007663712,0.000009621263,0.000002506395,0.9963908,0.0001811573,0.003049114,0.0002409331,0.000003614769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03899885,0.001622186,0.9533998,0.0005440179,0.0001318609,0.00006041906,0.0001671785,0.0007635704,0.004312149],"genre_scores_gemma":[0.8473294,0.0009932258,0.1434877,0.0003353029,0.0001089135,0.0001286336,0.0004603355,0.00005879172,0.007097886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01158389,"threshold_uncertainty_score":0.02303296,"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."}}