Existing fashion recommendation systems encounter difficulties in using visual data for accurate and personalized recommendations. This research describes an innovative end-to-end pipeline that uses artificial intelligence to provide fine-grained visual interpretation for fashion recommendations. When customers upload images of desired products or outfits, the system automatically generates meaningful descriptions emphasizing stylistic elements. These captions guide retrieval from a global fashion product catalogue to offer similar alternatives that fit the visual characteristics of the original image. On a dataset of over 100,000 categorized fashion photos, the pipeline was trained and evaluated. The F1-score for the object detection model was 0.97, exhibiting exact fashion object recognition capabilities optimized for recommendation. This visually aware system represents a key advancement in customer engagement through personalized fashion recommendations

翻译：现有时尚推荐系统在使用视觉数据进行精准个性化推荐时面临挑战。本研究描述了一种创新的端到端流水线，利用人工智能实现面向时尚推荐的细粒度视觉解读。当客户上传心仪商品或搭配的图片时，系统自动生成突显风格元素的语义描述。这些描述性文本引导从全球时尚产品目录中检索与原始图像视觉特征高度相似的替代商品。该流水线在包含逾10万张分类时尚照片的数据集上完成了训练与评估。目标检测模型的F1分数达到0.97，展现出针对推荐需求优化的精确时尚物品识别能力。这一视觉感知系统通过个性化时尚推荐代表了客户互动领域的关键进步。