{"id":"W4310496931","doi":"10.21203/rs.3.rs-2319674/v1","title":"Time-series association based dynamic graph evolution for recommendation","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 Saskatchewan","funders":"Fundamental Research Funds for the Central Universities","keywords":"Computer science; Graph; Association (psychology); Recommender system; Similarity (geometry); Series (stratigraphy); Data mining; Component (thermodynamics); Theoretical computer science; Information retrieval; Machine learning; Artificial intelligence","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":[0.001229121,0.0009826532,0.001413197,0.00256195,0.0005515845,0.0009264647,0.002081483,0.001607816,0.001894764],"category_scores_gemma":[0.006417139,0.000846953,0.001558209,0.003070533,0.0005927893,0.002008255,0.0005934188,0.002052907,0.0009029558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001363911,"about_ca_system_score_gemma":0.0006902303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03064557,"about_ca_topic_score_gemma":0.02718143,"domain_scores_codex":[0.9990793,0.0002308395,0.00004964305,0.0003553091,0.0002162533,0.00006856585],"domain_scores_gemma":[0.9967138,0.001985617,0.000366478,0.0003373907,0.0004815459,0.0001152527],"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":[0.0001388946,0.0001300876,0.006125018,0.000133987,0.0002256943,0.0001753652,0.0001509921,0.8341385,0.002595779,0.01711193,0.003616665,0.1354571],"study_design_scores_gemma":[0.000001747167,0.000005379478,0.0002266398,0.000002241089,0.000008626983,0.000009597332,0.000002373484,0.9972613,0.0001007484,0.002138492,0.0002397538,0.000003115643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0349251,0.0008081454,0.9610617,0.0003985845,0.00006386987,0.00004736395,0.0004659439,0.001130573,0.001098599],"genre_scores_gemma":[0.7983969,0.001056317,0.1914543,0.0002688657,0.0001747303,0.0001689608,0.001899001,0.0001951925,0.006385646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03064557,"threshold_uncertainty_score":0.06093442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04625572846620023,"score_gpt":0.3785042800707907,"score_spread":0.3322485516045905,"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."}}