{"id":"W4411030024","doi":"10.3390/fi17060252","title":"LLM4Rec: A Comprehensive Survey on the Integration of Large Language Models in Recommender Systems—Approaches, Applications and Challenges","year":2025,"lang":"en","type":"article","venue":"Future Internet","topic":"Topic Modeling","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Recommender system; Data science; World Wide Web; 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":[0.01069787,0.001568698,0.003353919,0.004624616,0.0009527848,0.004257842,0.003245852,0.00229343,0.006184257],"category_scores_gemma":[0.03108278,0.001569796,0.00263617,0.007820538,0.0006918411,0.007367736,0.00335551,0.003428503,0.00464347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002012837,"about_ca_system_score_gemma":0.002776462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009695108,"about_ca_topic_score_gemma":0.01120643,"domain_scores_codex":[0.9946138,0.002544152,0.0005740328,0.000691423,0.001395864,0.0001806848],"domain_scores_gemma":[0.974075,0.01962774,0.0004395415,0.002605386,0.002876416,0.0003760305],"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":[0.00009344733,0.0002093647,0.003901145,0.00461913,0.0005746577,0.0001076035,0.0005252674,0.01353063,0.001237568,0.03482985,0.03438845,0.9059828],"study_design_scores_gemma":[0.00005439606,0.0005579732,0.005969437,0.004473154,0.0009522496,0.0008608841,0.0008208827,0.2439812,0.003146738,0.09892873,0.6399163,0.0003381055],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.007112327,0.4136883,0.5525196,0.007145621,0.001049585,0.0003544225,0.001563574,0.003001625,0.01356506],"genre_scores_gemma":[0.0883171,0.4149622,0.4713511,0.003984499,0.003937433,0.0006878324,0.005630194,0.001192654,0.009936997],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01069787,"threshold_uncertainty_score":0.05657643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1154342309962003,"score_gpt":0.2923114499822141,"score_spread":0.1768772189860139,"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."}}