无需更新模型，用少量示例实时捕捉用户新兴趣。
Real-Time Personalization for LLM-based Recommendation with Customized In-Context Learning
- 将推荐数据转为上下文学习格式，保留LLM的即时学习能力。
- 实验显示无需模型更新即可实现快速个性化推荐。
- 适合需要实时响应的推荐场景，如电商、内容平台。
频繁更新基于大语言模型（LLM）的推荐系统以适应新用户兴趣在实践中不切实际，因训练成本过高，即使使用加速方法也是如此。本文探索通过上下文学习（ICL）在不更新模型的前提下适应动态用户兴趣，即利用输入中提供的少量示例让LLM学习新任务。通过将新兴趣示例作为ICL的少样本示例，可直接实现对用户兴趣的实时学习。然而，现有基于LLM的推荐系统在推荐调优过程中常丧失上下文学习能力，而原始LLM的上下文学习又缺乏推荐任务针对性。为此，我们提出RecICL，一种定制化的推荐专用上下文学习方法，用于实现实时推荐。RecICL将训练样本组织成上下文学习格式，确保在调优过程中保持并聚焦于推荐任务的上下文学习能力。大量实验表明，RecICL在无需模型更新的情况下有效实现实时推荐。代码已公开于 https://github.com/ym689/rec_icl。
原文摘要 · Abstract (English)
Frequently updating Large Language Model (LLM)-based recommender systems to adapt to new user interests -- as done for traditional ones -- is impractical due to high training costs, even with acceleration methods. This work explores adapting to dynamic user interests without any model updates by leveraging In-Context Learning (ICL), which allows LLMs to learn new tasks from few-shot examples provided in the input. Using new-interest examples as the ICL few-shot examples, LLMs may learn real-time interest directly, avoiding the need for model updates. However, existing LLM-based recommenders often lose the in-context learning ability during recommendation tuning, while the original LLM's in-context learning lacks recommendation-specific focus. To address this, we propose RecICL, which customizes recommendation-specific in-context learning for real-time recommendations. RecICL organizes training examples in an in-context learning format, ensuring that in-context learning ability is preserved and aligned with the recommendation task during tuning. Extensive experiments demonstrate RecICL's effectiveness in delivering real-time recommendations without requiring model updates. Our code is available at https://github.com/ym689/rec_icl.
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