用大模型特征增强推荐系统，不改结构也能提效。
LLM-KT: A Versatile Framework for Knowledge Transfer from Large Language Models to Collaborative Filtering
- 将大模型特征注入推荐模型中间层，内部重构使用。
- 在MovieLens和Amazon数据集上持续提升基线模型性能。
- 无需修改架构，适配多种推荐场景，易用性强。
我们提出LLM-KT，一种灵活的框架，通过无缝集成大语言模型（LLM）生成的特征来增强协同过滤（CF）模型。与现有方法不同，该框架不直接将LLM特征作为输入，而是将其注入任意CF模型的中间层，使模型能内部重构并利用这些嵌入表示。这种模型无关的方法无需修改网络结构，可适配多种推荐场景。框架设计便于集成与修改，为研究人员和开发者提供高效的知识迁移工具。在MovieLens和Amazon数据集上的实验表明，该方法能持续提升基线CF模型表现。相比当前先进方法，其在上下文感知设置下具有竞争力，且适用范围更广。
原文摘要 · Abstract (English)
We present LLM-KT, a flexible framework designed to enhance collaborative filtering (CF) models by seamlessly integrating LLM (Large Language Model)-generated features. Unlike existing methods that rely on passing LLM-generated features as direct inputs, our framework injects these features into an intermediate layer of any CF model, allowing the model to reconstruct and leverage the embeddings internally. This model-agnostic approach works with a wide range of CF models without requiring architectural changes, making it adaptable to various recommendation scenarios. Our framework is built for easy integration and modification, providing researchers and developers with a powerful tool for extending CF model capabilities through efficient knowledge transfer. We demonstrate its effectiveness through experiments on the MovieLens and Amazon datasets, where it consistently improves baseline CF models. Experimental studies showed that LLM-KT is competitive with the state-of-the-art methods in context-aware settings but can be applied to a broader range of CF models than current approaches.
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