Large language models (LLMs) have demonstrated impressive zero-shot abilities in solving a wide range of general-purpose tasks. However, it is empirically found that LLMs fall short in recognizing and utilizing temporal information, rendering poor performance in tasks that require an understanding of sequential data, such as sequential recommendation. In this paper, we aim to improve temporal awareness of LLMs by designing a principled prompting framework inspired by human cognitive processes. Specifically, we propose three prompting strategies to exploit temporal information within historical interactions for LLM-based sequential recommendation. Besides, we emulate divergent thinking by aggregating LLM ranking results derived from these strategies. Evaluations on MovieLens-1M and Amazon Review datasets indicate that our proposed method significantly enhances the zero-shot capabilities of LLMs in sequential recommendation tasks.

翻译：大语言模型（LLMs）在解决广泛通用任务时展现出令人印象深刻的零样本能力。然而，实证研究发现，LLMs在识别和利用时间信息方面存在不足，这导致其在需要理解序列数据的任务（如序列推荐）中表现不佳。本文旨在通过设计一种受人类认知过程启发的原则性提示框架，提升LLMs的时间感知能力。具体而言，我们提出三种提示策略，以在基于LLMs的序列推荐中利用历史交互中的时间信息。此外，我们通过聚合这些策略生成的LLM排序结果，模拟发散性思维。在MovieLens-1M和Amazon Review数据集上的评估表明，我们提出的方法显著增强了LLMs在序列推荐任务中的零样本能力。