将推荐系统视为新语言，让大模型理解用户行为序列。
Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models
- 用物品ID嵌入+文本特征构建独特提示，把行为序列当语言处理。
- 两阶段微调使模型融合传统推荐知识，提升序列预测效果。
- 适合想用大模型做个性化推荐的研究者与工程师。
本文探讨了使用大型语言模型（LLM）进行序列化推荐，即基于用户历史行为预测未来交互。我们提出新概念“将推荐系统作为大模型中的新语言”（RSLLM），结合传统推荐系统与大语言模型的优势。RSLLM采用独特的提示方法，融合传统推荐模型中的基于ID的物品嵌入与文本物品特征，并将用户的行为序列视为一种特定语言，通过投影器将物品嵌入对齐至大模型输入空间。此外，我们提出一个两阶段的大模型微调框架，利用两种对比损失与语言建模损失共同优化预训练大模型。首先使用纯文本提示进行微调，随后在目标领域使用统一提示进行进一步微调，从而让模型学习到传统序列推荐中的行为知识。实验结果验证了所提框架的有效性。
原文摘要 · Abstract (English)
This paper explores the use of Large Language Models (LLMs) for sequential recommendation, which predicts users' future interactions based on their past behavior. We introduce a new concept, "Integrating Recommendation Systems as a New Language in Large Models" (RSLLM), which combines the strengths of traditional recommenders and LLMs. RSLLM uses a unique prompting method that combines ID-based item embeddings from conventional recommendation models with textual item features. It treats users' sequential behaviors as a distinct language and aligns the ID embeddings with the LLM's input space using a projector. We also propose a two-stage LLM fine-tuning framework that refines a pretrained LLM using a combination of two contrastive losses and a language modeling loss. The LLM is first fine-tuned using text-only prompts, followed by target domain fine-tuning with unified prompts. This trains the model to incorporate behavioral knowledge from the traditional sequential recommender into the LLM. Our empirical results validate the effectiveness of our proposed framework.
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