{"id":"W4401857430","doi":"10.1145/3637528.3671474","title":"A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)","year":2024,"lang":"en","type":"review","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Recommender system; Computer science; Generative grammar; Generative model; Artificial intelligence; Key (lock); Multidisciplinary approach; Machine learning; Data science; Information retrieval","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009210017,0.000571226,0.002592719,0.0002662514,0.00005209417,0.0001802209,0.001542644,0.0002592417,0.00001397799],"category_scores_gemma":[0.0000208666,0.0003974932,0.0006996784,0.0006592433,0.00001949736,0.0004064148,0.0008319932,0.0004342592,0.00004257751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000269428,"about_ca_system_score_gemma":0.0007064849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006978348,"about_ca_topic_score_gemma":0.000001600414,"domain_scores_codex":[0.9962012,0.0004429597,0.001559045,0.001013824,0.0004251516,0.0003578368],"domain_scores_gemma":[0.9974827,0.00009247928,0.000548281,0.001571468,0.0001924184,0.0001126582],"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":[5.865892e-8,0.00001090336,1.851016e-9,0.2339215,0.0001642101,0.00000472955,0.00006731787,0.000854715,1.528669e-7,0.02459423,0.001839049,0.7385431],"study_design_scores_gemma":[0.00001556039,0.000005735227,5.135257e-11,0.1930126,0.0003411265,0.00005166646,0.000001297441,0.5433695,2.035926e-7,0.001322821,0.2616388,0.0002406768],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[3.153258e-9,0.5065739,0.4907336,0.00002990874,0.0006634131,0.000633701,0.000009352288,0.0000907844,0.00126537],"genre_scores_gemma":[4.051348e-7,0.917429,0.08133412,0.0002495299,0.0001863857,0.000104489,0.000009776157,0.00005152699,0.0006347381],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7383024,"threshold_uncertainty_score":0.9998477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3534450477250533,"score_gpt":0.4060754439442824,"score_spread":0.05263039621922905,"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."}}