Large Language Models (LLMs) have shown promising potential in E-commerce community recommendation. While LLMs and Multimodal LLMs (MLLMs) are widely used to encode notes into implicit embeddings, leveraging their generative capabilities to represent notes with interpretable tags remains unexplored. In the field of tag generation, traditional close-ended methods heavily rely on the design of tag pools, while existing open-ended methods applied directly to note recommendations face two limitations: (1) MLLMs lack guidance during generation, resulting in redundant tags that fail to capture user interests; (2) The generated tags are often coarse and lack fine-grained representation of notes, interfering with downstream recommendations. To address these limitations, we propose TagLLM, a fine-grained tag generation method for note recommendation. TagLLM captures user interests across note categories through a User Interest Handbook and constructs fine-grained tag data using multimodal CoT Extraction. A Tag Knowledge Distillation method is developed to equip small models with competitive generation capabilities, enhancing inference efficiency. In online A/B test, TagLLM increases average view duration per user by 0.31%, average interactions per user by 0.96%, and page view click-through rate in cold-start scenario by 32.37%, demonstrating its effectiveness.

翻译：大语言模型（LLMs）在电商社区推荐中展现了巨大潜力。虽然LLMs与多模态大语言模型（MLLMs）被广泛用于将笔记编码为隐式嵌入，但其利用生成能力以可解释标签表征笔记的潜力尚未被探索。在标签生成领域，传统封闭式方法严重依赖标签池设计，而现有直接应用于笔记推荐的开放式方法存在两大局限：（1）MLLMs在生成过程中缺乏引导，导致生成冗余标签且无法捕捉用户兴趣；（2）生成的标签通常较为粗糙，缺乏对笔记的细粒度表征，干扰下游推荐。为解决上述问题，我们提出TagLLM——一种面向笔记推荐的细粒度标签生成方法。TagLLM通过用户兴趣手册捕捉用户跨笔记类别的兴趣，并利用多模态思维链提取构建细粒度标签数据。我们开发了一种标签知识蒸馏方法，使小模型具备竞争力的生成能力，从而提升推理效率。在线A/B测试中，TagLLM使每位用户平均观看时长提升0.31%，平均交互次数提升0.96%，冷启动场景下页面浏览点击率提升32.37%，验证了其有效性。