{"id":"W4307187574","doi":"10.36227/techrxiv.21342033.v1","title":"Heterogeneous Graph Neural Network with Time Sequence Information Integration","year":2022,"lang":"en","type":"preprint","venue":"","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; Recurrent neural network; Data mining; Artificial neural network; Artificial intelligence; Machine learning; 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":[0.0005200043,0.0008651617,0.001002073,0.0008898384,0.000399093,0.0009157773,0.0013624,0.001097432,0.002206768],"category_scores_gemma":[0.002189712,0.0005271756,0.0007591025,0.001434649,0.0005189157,0.001783634,0.0008317553,0.001373675,0.0003607924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001539691,"about_ca_system_score_gemma":0.0008897221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03673849,"about_ca_topic_score_gemma":0.02908809,"domain_scores_codex":[0.9995733,0.00007897898,0.00002403418,0.0001782649,0.00007987308,0.00006554648],"domain_scores_gemma":[0.9994547,0.0002601693,0.00007232877,0.00004624806,0.0001368168,0.00002978933],"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":[0.00006320341,0.00004978248,0.0007403052,0.00003593125,0.00005011688,0.00008271581,0.00003000735,0.9498097,0.0005312073,0.008495091,0.000934698,0.03917727],"study_design_scores_gemma":[0.000001671941,0.000004009669,0.00004842,8.538984e-7,0.000003809919,0.000002826859,0.000001162907,0.9981572,0.00005127538,0.001634091,0.00009289385,0.000001763666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0618015,0.001439345,0.9291229,0.0007567526,0.0001940819,0.00006104619,0.0005275895,0.001095137,0.005001707],"genre_scores_gemma":[0.9187581,0.000627585,0.07117254,0.0002604688,0.00008401411,0.0001316267,0.0008021776,0.00007063628,0.008092865],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03673849,"threshold_uncertainty_score":0.07304931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02247411582560568,"score_gpt":0.2444828777574564,"score_spread":0.2220087619318508,"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."}}