{"id":"W4415883353","doi":"10.1109/access.2025.3628684","title":"LLM-SCRec: LLM-Powered Style-Context Insights for Fashion Recommendation","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Embedding; Encoder; Encoding (memory); Natural language; Simple (philosophy); Space (punctuation); Recommender system; Expressive power","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":[0.0003360318,0.0001941479,0.0002704148,0.0002842157,0.0002251384,0.0006939069,0.001436114,0.0001329613,0.00001613392],"category_scores_gemma":[0.00003742338,0.0001739196,0.0001102456,0.0004832632,0.00001744724,0.001646275,0.0002305111,0.0001260101,0.00001338707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009852405,"about_ca_system_score_gemma":0.00007498536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001439653,"about_ca_topic_score_gemma":0.0002123884,"domain_scores_codex":[0.9985411,0.0001022289,0.0004246651,0.0005190841,0.0001350718,0.0002778229],"domain_scores_gemma":[0.9987515,0.0002028161,0.0001910433,0.000606696,0.0001829157,0.00006501414],"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":[0.00002556354,0.0002533963,0.0005920202,0.0001198553,0.00007239827,0.00000240758,0.0006078776,0.000002947778,0.0009054546,0.08080308,0.1411314,0.7754835],"study_design_scores_gemma":[0.0018412,0.0003813868,0.001783639,0.0002706966,0.0000269881,0.000006968557,0.0001630145,0.01711776,0.07582566,0.03969293,0.8621452,0.0007445232],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007807502,0.0001037622,0.9730785,0.003217394,0.003065727,0.0008334712,0.000005946101,0.0004698888,0.01141782],"genre_scores_gemma":[0.9899483,0.00004687645,0.006844947,0.001746858,0.0001378835,0.0003064256,0.00001881523,0.00001344289,0.0009364344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9821408,"threshold_uncertainty_score":0.7092235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0410088996420746,"score_gpt":0.3362258736212379,"score_spread":0.2952169739791633,"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."}}