用大模型增强新物品推荐，仅靠少量互动也能学出好特征。
LLM-Empowered Representation Learning for Emerging Item Recommendation
- 用大模型推理丰富新物品原始特征，生成独特嵌入
- 通过元学习融合新交互，实现少样本高效更新
- 在电影、药品等场景中显著优于现有方法
本文针对新兴物品推荐问题展开研究，其交互数据随时间逐步积累。现有方法通常假设新物品历史交互极少甚至为零，这一简化忽略了其动态演变过程。一个优秀模型需在保留新物品独特性的同时，利用其与成熟物品的共性模式。为此，我们提出EmerFlow——一种基于大语言模型的表示学习框架，通过大模型推理增强新物品原始特征，并将其嵌入空间对齐至已有推荐模型；随后通过元学习融入新交互以优化嵌入。该方法仅依赖有限交互即可学习到表达能力强的嵌入。在电影、药品等多个领域进行的大量实验表明，EmerFlow持续优于现有方法。
原文摘要 · Abstract (English)
In this work, we tackle the challenge of recommending emerging items, whose interactions gradually accumulate over time. Existing methods often overlook this dynamic process, typically assuming that emerging items have few or even no historical interactions. Such an assumption oversimplifies the problem, as a good model must preserve the uniqueness of emerging items while leveraging their shared patterns with established ones. To address this challenge, we propose EmerFlow, a novel LLM-empowered representation learning framework that generates distinctive embeddings for emerging items. It first enriches the raw features of emerging items through LLM reasoning, then aligns these representations with the embedding space of the existing recommendation model. Finally, new interactions are incorporated through meta-learning to refine the embeddings. This enables EmerFlow to learn expressive embeddings for emerging items from only limited interactions. Extensive experiments across diverse domains, including movies and pharmaceuticals, show that EmerFlow consistently outperforms existing methods.
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