{"id":"W4320495209","doi":"10.20944/preprints202302.0196.v1","title":"SFRRG: A Graph Neural Network Recommendation Model based on Feature and Structure Information","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Collaborative filtering; Computer science; Recommender system; Information overload; Feature (linguistics); Graph; The Internet; Artificial neural network; Data mining; Machine learning; Algorithm; Artificial intelligence; Information retrieval; Theoretical computer science; World Wide Web","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007379054,0.0004258334,0.0004051139,0.0003452261,0.0001978151,0.0002800734,0.001021044,0.0005289899,0.00001952336],"category_scores_gemma":[0.00006402854,0.0004022892,0.0001494275,0.000337684,0.00002834002,0.0006946091,0.001944394,0.001172214,0.00004284618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001017651,"about_ca_system_score_gemma":0.00009673566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001023835,"about_ca_topic_score_gemma":0.00002828581,"domain_scores_codex":[0.9977257,0.0001954736,0.0005072218,0.0008434338,0.0003503651,0.0003778517],"domain_scores_gemma":[0.9976704,0.00008669034,0.0005032871,0.001449849,0.0001575242,0.000132261],"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":[0.0001472942,0.0001432412,0.157032,0.001628687,0.0002868457,0.0000159364,0.004311163,0.6749612,0.000307516,0.0260271,0.03829619,0.09684286],"study_design_scores_gemma":[0.0002393107,0.00002938304,0.03696147,0.0002390287,0.00001415795,0.000005636811,0.00001204849,0.9229161,0.0004377955,0.03597385,0.002726998,0.0004442785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08288306,0.00004598836,0.8868511,0.01773224,0.003650134,0.002406835,0.0001398744,0.002955818,0.003334896],"genre_scores_gemma":[0.9811417,0.00005172999,0.01683703,0.001171994,0.0001620664,0.0001841424,0.0002955428,0.00003026594,0.0001255627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8982586,"threshold_uncertainty_score":0.9998429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09380719434873871,"score_gpt":0.3183075620599956,"score_spread":0.2245003677112569,"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."}}