{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007545603,0.0001922459,0.0002539158,0.0001925863,0.0004938833,0.000198701,0.001201338,0.00002185188,0.0002078395],"category_scores_gemma":[0.000004936771,0.0001653174,0.0001169872,0.0004025519,0.00001124052,0.000263958,0.0006660781,0.0002288905,0.00003320878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002713974,"about_ca_system_score_gemma":0.0001062171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002808273,"about_ca_topic_score_gemma":0.00001400573,"domain_scores_codex":[0.9979341,0.0003618214,0.0003855351,0.0005145849,0.0004342792,0.0003696581],"domain_scores_gemma":[0.9986624,0.0001235748,0.0001620449,0.0008721643,0.00005495581,0.0001248706],"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":[0.00004934502,0.0008889992,0.004309593,0.0005242901,0.0001993462,0.0008837202,0.00203705,0.004480762,0.0006116463,0.1770628,0.4037838,0.4051686],"study_design_scores_gemma":[0.0006999438,0.0002616502,0.0001439184,0.00004716582,0.000006342792,0.0002662809,0.0005697756,0.7814817,0.004965794,0.002282109,0.2086531,0.0006221742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001039285,0.00009187363,0.9716424,0.001972606,0.0009435581,0.0003554389,0.000007865832,0.001647233,0.02229975],"genre_scores_gemma":[0.9621265,0.000001707442,0.03483305,0.001758655,0.00003308403,0.000339646,0.000007238475,0.00002146853,0.0008786265],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9610872,"threshold_uncertainty_score":0.6741447,"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."}}