将用户和物品嵌入投影到大模型空间，提升推荐效果
Do LLMs Benefit from User and Item Embeddings in Recommendation Tasks?
- 用轻量投影模块将协同过滤嵌入映射到大模型词空间
- 相比纯文本大模型，推荐性能显著提升
- 适合想融合传统推荐与大模型的开发者
大型语言模型（LLMs）作为推荐系统展现出潜力，通过生成方式建模用户偏好。然而，现有方法多依赖文本语义或有限使用用户/物品嵌入，难以处理用户历史中的多个物品嵌入，退化为仅依赖文本语义，忽略更丰富的协同信息。本文提出一种简单有效的方法：通过独立的轻量级投影模块，将协同过滤学习的用户和物品嵌入映射到LLM的词空间。微调后的LLM在生成推荐时，同时利用投影嵌入与文本标记。初步结果表明，该设计能有效利用结构化的用户-物品交互数据，在推荐性能上优于纯文本的LLM基线，为传统推荐系统与现代大模型的融合提供可行路径。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have emerged as promising recommendation systems, offering novel ways to model user preferences through generative approaches. However, many existing methods often rely solely on text semantics or incorporate collaborative signals in a limited manner, typically using only user or item embeddings. These methods struggle to handle multiple item embeddings representing user history, reverting to textual semantics and neglecting richer collaborative information. In this work, we propose a simple yet effective solution that projects user and item embeddings, learned from collaborative filtering, into the LLM token space via separate lightweight projector modules. A finetuned LLM then conditions on these projected embeddings alongside textual tokens to generate recommendations. Preliminary results show that this design effectively leverages structured user-item interaction data, improves recommendation performance over text-only LLM baselines, and offers a practical path for bridging traditional recommendation systems with modern LLMs.
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