{"id":"W4306660016","doi":"10.21203/rs.3.rs-2171851/v1","title":"Heterogeneous Graph-Neural-Network with TimeSequence Information Integration","year":2022,"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":"University of Calgary","funders":"","keywords":"Computer science; Recommender system; Collaborative filtering; Graph; Information retrieval; Data mining; Machine learning; Artificial intelligence; 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":[0.0006866399,0.0008551463,0.000961238,0.0008993954,0.0004522557,0.0008414119,0.001386613,0.001292075,0.001970343],"category_scores_gemma":[0.002491402,0.0005170858,0.0007225423,0.001347004,0.000586455,0.001684288,0.0008408629,0.001315172,0.0002894717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001789791,"about_ca_system_score_gemma":0.0007689961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04316692,"about_ca_topic_score_gemma":0.03676049,"domain_scores_codex":[0.9995558,0.00009812231,0.00002694437,0.0001776986,0.00007092136,0.00007061368],"domain_scores_gemma":[0.9991773,0.0004222775,0.0001011077,0.00007071348,0.0001887955,0.00003974516],"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":[0.00005362974,0.00005333086,0.0006649338,0.00002542383,0.00004556031,0.00005474427,0.00001887618,0.9638076,0.0003857182,0.004492807,0.0006733637,0.02972402],"study_design_scores_gemma":[0.000001202793,0.000003195081,0.00004079245,6.244837e-7,0.000002654017,0.000001515279,8.801351e-7,0.9989203,0.00004512446,0.0009394346,0.00004315047,0.000001101562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1078053,0.001193494,0.8835742,0.0009984416,0.0001685084,0.00006453807,0.0005420161,0.00122104,0.004432441],"genre_scores_gemma":[0.9406259,0.0002649865,0.05405948,0.0001982827,0.00005498239,0.00007541493,0.0005171192,0.0000456834,0.00415828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04316692,"threshold_uncertainty_score":0.08583128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0575644708903619,"score_gpt":0.3539430228556462,"score_spread":0.2963785519652843,"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."}}