{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007601822,0.0009889553,0.001326777,0.001299949,0.000510912,0.0008308526,0.002458134,0.001723635,0.002172948],"category_scores_gemma":[0.002175136,0.000477842,0.0008817044,0.001811989,0.0004543053,0.001755149,0.0005355541,0.001667144,0.0008490409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001218098,"about_ca_system_score_gemma":0.001195416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04341439,"about_ca_topic_score_gemma":0.04757143,"domain_scores_codex":[0.9995609,0.0001110926,0.00002482784,0.0001321818,0.0001244298,0.00004667152],"domain_scores_gemma":[0.9995438,0.0001802839,0.00004817922,0.00003769798,0.0001662448,0.00002382675],"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":[0.0001836053,0.0001622956,0.002264955,0.0002074252,0.0001985538,0.0001336541,0.00007567705,0.7458904,0.002609229,0.01018446,0.00718969,0.2309],"study_design_scores_gemma":[0.00001268522,0.00003730131,0.0001987415,0.000008534265,0.00002123306,0.00002540681,0.00000357499,0.9965971,0.0002554921,0.002090541,0.0007403417,0.000009015062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02122062,0.002052144,0.9690534,0.0008627132,0.0002014552,0.0001547704,0.0005262073,0.001360603,0.00456817],"genre_scores_gemma":[0.5479842,0.003277856,0.4305076,0.0007351642,0.0002444773,0.0005151033,0.001565801,0.0001453606,0.01502444],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04341439,"threshold_uncertainty_score":0.08632338,"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."}}