Large language models (LLMs) have enhanced conventional recommendation models via user profiling, which generates representative textual profiles from users' historical interactions. However, their direct application to session-based recommendation (SBR) remains challenging due to severe session context scarcity and poor scalability. In this paper, we propose SPRINT, a scalable SBR framework that incorporates reliable and informative intents while ensuring high efficiency in both training and inference. SPRINT constrains LLM-based profiling with a global intent pool and validates inferred intents based on recommendation performance to mitigate noise and hallucinations under limited context. To ensure scalability, LLMs are selectively invoked only for uncertain sessions during training, while a lightweight intent predictor generalizes intent prediction to all sessions without LLM dependency at inference time. Experiments on real-world datasets show that SPRINT consistently outperforms state-of-the-art methods while providing more explainable recommendations.

翻译：大型语言模型（LLMs）通过用户画像技术增强了传统推荐模型，该技术从用户历史交互中生成具有代表性的文本画像。然而，由于会话上下文严重稀缺且可扩展性较差，此类模型在会话推荐（SBR）中的直接应用仍面临挑战。本文提出SPRINT框架——一种可扩展的SBR框架，其在保证训练与推理阶段高效性的同时，融入了可靠且信息丰富的意图。SPRINT通过全局意图池约束基于LLM的画像生成，并根据推荐性能验证推断出的意图，以缓解有限上下文下的噪声与幻觉问题。为确保可扩展性，LLM仅在训练阶段被选择性调用以处理不确定会话，而轻量级意图预测器在推理阶段无需依赖LLM即可推广至所有会话的意图预测。在真实数据集上的实验表明，SPRINT在提供更强可解释性推荐的同时，持续优于现有最先进方法。