{"id":"W4413277286","doi":"10.1109/access.2025.3599832","title":"A Comprehensive Survey on LLM-Powered Recommender Systems: From Discriminative, Generative to Multi-Modal Paradigms","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Topic Modeling","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Toronto Metropolitan University","keywords":"Computer science; Discriminative model; Recommender system; Modal; Generative grammar; Artificial intelligence; Machine learning; Information retrieval","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":[0.00304517,0.001247738,0.001587483,0.002547498,0.0007725456,0.002286684,0.002093326,0.001497257,0.005003599],"category_scores_gemma":[0.01107623,0.0009740721,0.001206506,0.005644729,0.0004871859,0.004277855,0.001583975,0.00165586,0.002807035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009945953,"about_ca_system_score_gemma":0.001137868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004726688,"about_ca_topic_score_gemma":0.006977814,"domain_scores_codex":[0.9980987,0.0007004844,0.000207141,0.0003598801,0.0005422679,0.00009148835],"domain_scores_gemma":[0.9939016,0.004208094,0.0001488379,0.0007956229,0.0008390543,0.0001068266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000146762,0.0001679711,0.00430571,0.004375887,0.0002757763,0.00008781306,0.0003879763,0.0160197,0.002423562,0.02894973,0.02199506,0.920864],"study_design_scores_gemma":[0.0001042575,0.0008995987,0.009890835,0.003738587,0.0008945875,0.002099902,0.0008820859,0.4468189,0.007885227,0.09073218,0.4356616,0.0003922651],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01849324,0.3692044,0.5856191,0.004193864,0.0006245024,0.0004005313,0.001225319,0.002714164,0.01752486],"genre_scores_gemma":[0.1946109,0.3490579,0.4370822,0.002662194,0.00214248,0.0005599065,0.003017,0.0005311103,0.01033621],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005003599,"threshold_uncertainty_score":0.01673871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1536053497693526,"score_gpt":0.3817094154353873,"score_spread":0.2281040656660346,"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."}}