用大模型+特定提示词，让服装推荐更精准
Sequential LLM Framework for Fashion Recommendation
- 用预训练大模型加服装领域提示词，提升推荐理解力
- 结合混合检索技术，文本描述能准确匹配商品
- 适合做时尚电商推荐系统的研究者和开发者
时尚产业是全球电子商务领域的领先行业，推动各大在线零售商采用推荐系统以提供产品建议并提升客户体验。尽管推荐系统已被广泛研究，但大多数针对通用电商问题设计，难以应对时尚领域的独特挑战。为此，我们提出一种基于预训练大语言模型（LLM）的序列化时尚推荐框架，通过推荐专用提示词增强模型能力。该框架采用参数高效微调，并利用大量时尚数据进行训练，同时引入一种新颖的基于混合法（mix-up）的检索技术，实现从文本描述到相关商品建议的精准转换。大量实验表明，所提框架显著提升了时尚推荐性能。
原文摘要 · Abstract (English)
The fashion industry is one of the leading domains in the global e-commerce sector, prompting major online retailers to employ recommendation systems for product suggestions and customer convenience. While recommendation systems have been widely studied, most are designed for general e-commerce problems and struggle with the unique challenges of the fashion domain. To address these issues, we propose a sequential fashion recommendation framework that leverages a pre-trained large language model (LLM) enhanced with recommendation-specific prompts. Our framework employs parameter-efficient fine-tuning with extensive fashion data and introduces a novel mix-up-based retrieval technique for translating text into relevant product suggestions. Extensive experiments show our proposed framework significantly enhances fashion recommendation performance.
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