LLM-SCRec: LLM-Powered Style-Context Insights for Fashion Recommendation

Fashion recommendation is increasingly shaped by platform-native narratives such as titles, captions, tags, and reviews, where users implicitly reveal their style preferences and the contexts in which items are chosen. However, existing systems often simplify such text into predefined labels or treat it as auxiliary metadata, which leads to semantic loss and limits the ability to model fine-grained, context-dependent preferences. This gap prevents recommendation models from fully exploiting the expressive power of natural language to capture style intent. To address these challenges, we propose LLM-SCRec, an interaction-centric framework that directly leverages large language models to extract <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">style–context insights</i> from raw user–item interaction text. Specifically, (1) a frozen LLM reads platform-native descriptions and generates high-dimensional interaction-level insights; (2) a lightweight encoder projects these insights into a compact embedding space and aligns them with collaborative signals through contrastive objectives; and (3) aggregated user/item representations are seamlessly integrated with existing ID-based recommenders through simple concatenation. Extensive experiments across multiple benchmarks show that this framework consistently improves performance on standard Top-<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> metrics relative to strong baselines. Beyond accuracy, its plug-and-play nature makes the method practical: because it does not alter the backbone recommenders and requires only offline LLM processing, it offers an efficient and easily deployable solution.