{"id":"W4309640192","doi":"10.1109/iemcon56893.2022.9946615","title":"SessNet: A Deep Hybrid-state Session-based Recommender System","year":2022,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo; Royal Bank of Canada; Vector Institute","funders":"","keywords":"Computer science; Session (web analytics); Recommender system; Benchmark (surveying); Context (archaeology); Recurrent neural network; State (computer science); Artificial intelligence; Machine learning; Multimedia; Artificial neural network; World Wide Web; Algorithm","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":[0.0004569832,0.0007073143,0.0007673549,0.0005504046,0.0003154192,0.0004523049,0.001594516,0.0007448615,0.00196555],"category_scores_gemma":[0.001255551,0.0004510384,0.0005525426,0.0006134693,0.000167543,0.001176917,0.0007696057,0.00113569,0.00126041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005302378,"about_ca_system_score_gemma":0.0008077895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0184494,"about_ca_topic_score_gemma":0.05733326,"domain_scores_codex":[0.9997782,0.00004440814,0.0000148988,0.00007722055,0.00005692154,0.00002836216],"domain_scores_gemma":[0.9996372,0.0001128383,0.00002990985,0.00009953819,0.00008507662,0.00003540425],"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":[0.001085898,0.001012612,0.01566232,0.0003901092,0.0007861502,0.0003494381,0.000231478,0.2066903,0.01735822,0.006324518,0.04112879,0.7089801],"study_design_scores_gemma":[0.00003716399,0.0001747518,0.002296671,0.00001362914,0.0000709741,0.0001234368,0.00001598843,0.9860867,0.002830504,0.002205594,0.006110146,0.00003448583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1416284,0.003225953,0.8125836,0.0007021116,0.000370017,0.0003060225,0.006507177,0.02617404,0.008502583],"genre_scores_gemma":[0.7257897,0.001008044,0.24871,0.0005010872,0.0001077384,0.0002289155,0.01003442,0.0001951249,0.01342502],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0184494,"threshold_uncertainty_score":0.03668404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01427047915626238,"score_gpt":0.2306586997848969,"score_spread":0.2163882206286345,"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."}}