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（LLM）通过用户画像生成技术，从用户历史交互中生成具有代表性的文本画像，从而增强了传统推荐模型。然而，由于会话上下文严重匮乏且可扩展性较差，将其直接应用于会话推荐（SBR）仍面临挑战。本文提出SPRINT，一种可扩展的会话推荐框架，该框架在保证训练与推理高效性的同时，融合了可靠且信息丰富的用户意图。SPRINT通过全局意图池对基于LLM的画像生成进行约束，并依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，训练阶段仅对不确定性较高的会话选择性调用LLM，而在推理阶段则通过轻量级意图预测器实现对所有会话的意图预测，无需依赖LLM。在真实数据集上的实验表明，SPRINT在持续超越现有最优方法的同时，能够提供更具可解释性的推荐结果。