解码风格：面向带偏好的图像引导穿搭推荐的LLM高效微调
Decoding Style: Efficient Fine-Tuning of LLMs for Image-Guided Outfit Recommendation with Preference
AI总结：
本研究提出一种图像引导的个性化穿搭推荐框架，通过MLLM弥合视觉-文本鸿沟，在Polyvore数据集上高效微调LLM并结合直接偏好机制构建自增强反馈循环，在两项核心任务上性能显著优于基础LLM。
摘要：
个性化穿搭推荐仍是一项复杂挑战，既需要理解时尚兼容性，也需要具备趋势感知能力。本文提出一种新型框架，利用大语言模型（LLM）的表达能力完成该任务，通过微调和直接反馈整合缓解其“黑箱”属性与静态特性。\n我们采用多模态大语言模型（MLLM）生成图像字幕，弥合物品描述中的视觉-文本鸿沟。这使得LLM能够从人工整理的时尚图像中提取风格与色彩特征，为个性化推荐奠定基础。我们在开源的Polyvore精选时尚图像数据集上对LLM进行高效微调，优化其推荐时尚穿搭的能力。框架还采用基于负例的直接偏好机制优化LLM的决策流程，构建自我增强的AI反馈循环，可根据季节时尚趋势持续优化推荐结果。\n我们在Polyvore数据集上对框架进行评估，在填空式穿搭推荐、互补单品检索两项核心任务中验证了其有效性。评估结果表明，所提框架生成的穿搭更具时尚感、契合趋势，且能通过直接反馈持续提升，性能显著优于基础LLM，搭配协调性更强。这些任务上的性能提升凸显了该框架凭借精准推荐优化购物体验的潜力，证明其效果优于基于普通LLM的穿搭生成方法。
英文摘要：
Personalized outfit recommendation remains a complex challenge, demanding both fashion compatibility understanding and trend awareness. This paper presents a novel framework that harnesses the expressive power of large language models (LLMs) for this task, mitigating their "black box" and static nature through fine-tuning and direct feedback integration. We bridge the item visual-textual gap in items descriptions by employing image captioning with a Multimodal Large Language Model (MLLM). This enables the LLM to extract style and color characteristics from human-curated fashion images, forming the basis for personalized recommendations. The LLM is efficiently fine-tuned on the open-source Polyvore dataset of curated fashion images, optimizing its ability to recommend stylish outfits. A direct preference mechanism using negative examples is employed to enhance the LLM's decision-making process. This creates a self-enhancing AI feedback loop that continuously refines recommendations in line with seasonal fashion trends. Our framework is evaluated on the Polyvore dataset, demonstrating its effectiveness in two key tasks: fill-in-the-blank, and complementary item retrieval. These evaluations underline the framework's ability to generate stylish, trend-aligned outfit suggestions, continuously improving through direct feedback. The evaluation results demonstrated that our proposed framework significantly outperforms the base LLM, creating more cohesive outfits. The improved performance in these tasks underscores the proposed framework's potential to enhance the shopping experience with accurate suggestions, proving its effectiveness over the vanilla LLM based outfit generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
