{"id":"W4200384734","doi":"10.32920/17131607.v1","title":"SessNet: A Hybrid Session-Based Recommender System","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Session (web analytics); Computer science; Recommender system; Benchmark (surveying); Profiling (computer programming); Transformer; Recurrent neural network; Information retrieval; Machine learning; Artificial intelligence; Artificial neural network; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009512528,0.0009403026,0.001228109,0.001020773,0.00044566,0.0006044679,0.0020029,0.001097867,0.002290113],"category_scores_gemma":[0.002321439,0.0004923994,0.0006870634,0.001082646,0.0001761947,0.001899488,0.0009143793,0.001336653,0.002174908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005418345,"about_ca_system_score_gemma":0.001079845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02092626,"about_ca_topic_score_gemma":0.06446885,"domain_scores_codex":[0.9994617,0.0001207356,0.00004351401,0.000183998,0.0001456409,0.00004445148],"domain_scores_gemma":[0.9991925,0.0002339957,0.00004936667,0.0002222092,0.0002260651,0.00007591645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00144713,0.001290391,0.01942126,0.0006279804,0.0009467913,0.0004398357,0.0003243024,0.1079695,0.01832998,0.007041647,0.07033476,0.7718265],"study_design_scores_gemma":[0.00007566866,0.0002802785,0.00342296,0.00002949629,0.0001401681,0.0002859164,0.00004339754,0.9723384,0.004150933,0.003550333,0.01560004,0.00008252206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.121147,0.004425792,0.8230792,0.00099631,0.0005878762,0.0005418885,0.01197395,0.02749328,0.009754666],"genre_scores_gemma":[0.550367,0.001702155,0.4077122,0.000709268,0.0002377144,0.0003342009,0.01627694,0.0002955724,0.02236499],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02092626,"threshold_uncertainty_score":0.04160893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02804844349899883,"score_gpt":0.2643550680340511,"score_spread":0.2363066245350523,"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."}}