{"id":"W4362551547","doi":"10.21203/rs.3.rs-2771529/v1","title":"Interest-Aware Message Passing Recommender Model based on Graph Convolutional Networks","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Computer science; Recommender system; Graph; Theoretical computer science; Collaborative filtering; Feature learning; Graph partition; Smoothing; Representation (politics); Partition (number theory); Node (physics); Artificial intelligence; Machine learning","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","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004279321,0.0004921076,0.0005726128,0.00127624,0.0005793348,0.001093781,0.002845092,0.0007153935,0.00002861511],"category_scores_gemma":[0.0001412442,0.000451475,0.0004092178,0.0009121072,0.0001478909,0.0002547943,0.003881035,0.003506222,0.00005922085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006130262,"about_ca_system_score_gemma":0.000715892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000274655,"about_ca_topic_score_gemma":0.00009900652,"domain_scores_codex":[0.9937524,0.001324256,0.0006486748,0.001559673,0.001542758,0.001172251],"domain_scores_gemma":[0.9954082,0.001052806,0.0002183515,0.002303627,0.0006760163,0.000341033],"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":[0.0001002333,0.000634728,0.001379883,0.001719943,0.000248997,0.0003184332,0.0004686513,0.2932597,0.0000157083,0.1386402,0.5240778,0.03913572],"study_design_scores_gemma":[0.0002225301,0.0001612716,0.0003423836,0.002099002,0.00000373983,0.000002913139,0.00004551019,0.9377296,0.00004269836,0.05729032,0.001646905,0.0004131069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001583922,0.0002157948,0.984562,0.009286493,0.001029943,0.001114906,0.00008184943,0.001265161,0.002285486],"genre_scores_gemma":[0.982669,0.0002061757,0.01439156,0.0003819934,0.0003556674,0.0009527128,0.000219858,0.0001026025,0.000720419],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9825106,"threshold_uncertainty_score":0.9999432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2402196307621704,"score_gpt":0.4157741533025658,"score_spread":0.1755545225403955,"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."}}