{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006476408,0.0002260228,0.0003262585,0.000867016,0.0006860576,0.0005306367,0.001236527,0.0003314628,0.0002024447],"category_scores_gemma":[0.0004534373,0.000242979,0.0002634733,0.0007096085,0.00003028113,0.0004428235,0.001475489,0.00105224,0.00002673435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00329459,"about_ca_system_score_gemma":0.0005700216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001954555,"about_ca_topic_score_gemma":0.00007572184,"domain_scores_codex":[0.9958019,0.001264032,0.0004385422,0.0008283433,0.001061832,0.000605347],"domain_scores_gemma":[0.9971545,0.0006538075,0.0003522721,0.0009292642,0.0008148344,0.00009529476],"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":[0.0003458172,0.001668202,0.01116175,0.007700554,0.0007268983,0.00003048059,0.002707551,0.003466924,0.001483607,0.1059438,0.6926717,0.1720928],"study_design_scores_gemma":[0.0008448776,0.001272933,0.005507476,0.0005227443,0.00002105429,0.000004334237,0.0003119304,0.5810073,0.0006397437,0.1651471,0.2437337,0.0009868009],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000545557,0.0002315208,0.9786027,0.01294562,0.001075973,0.003100127,0.0003321617,0.0007609311,0.002405379],"genre_scores_gemma":[0.6975126,0.000542782,0.2500522,0.0003155787,0.0008486887,0.02371892,0.008165434,0.000266805,0.01857695],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7285506,"threshold_uncertainty_score":0.9908395,"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."}}