Large language models (LLMs) have achieved impressive zero-shot performance in various natural language processing (NLP) tasks, demonstrating their capabilities for inference without training examples. Despite their success, no research has yet explored the potential of LLMs to perform next-item recommendations in the zero-shot setting. We have identified two major challenges that must be addressed to enable LLMs to act effectively as recommenders. First, the recommendation space can be extremely large for LLMs, and LLMs do not know about the target user's past interacted items and preferences. To address this gap, we propose a prompting strategy called Zero-Shot Next-Item Recommendation (NIR) prompting that directs LLMs to make next-item recommendations. Specifically, the NIR-based strategy involves using an external module to generate candidate items based on user-filtering or item-filtering. Our strategy incorporates a 3-step prompting that guides GPT-3 to carry subtasks that capture the user's preferences, select representative previously watched movies, and recommend a ranked list of 10 movies. We evaluate the proposed approach using GPT-3 on MovieLens 100K dataset and show that it achieves strong zero-shot performance, even outperforming some strong sequential recommendation models trained on the entire training dataset. These promising results highlight the ample research opportunities to use LLMs as recommenders. The code can be found at https://github.com/AGI-Edgerunners/LLM-Next-Item-Rec.

翻译：大型语言模型（LLMs）在各类自然语言处理（NLP）任务中展现出令人瞩目的零样本性能，证明了其在无需训练样本的情况下进行推理的能力。尽管取得了成功，但尚无研究探索LLMs在零样本场景下执行下一项推荐任务的潜力。我们识别出两大关键挑战，必须加以解决才能让LLMs有效发挥推荐功能。首先，LLMs的推荐空间可能极为庞大，且模型不了解目标用户的历史交互项目与偏好。为弥补这一不足，我们提出了一种名为“零样本下一项推荐（NIR）”的提示策略，引导LLMs进行下一项推荐。具体而言，基于NIR的策略通过外部模块，依据用户过滤或项目过滤生成候选项目。我们的策略包含三步提示，引导GPT-3依次完成以下子任务：捕捉用户偏好、选取代表性历史观看电影，并推荐一份包含10部电影的排序列表。我们在MovieLens 100K数据集上使用GPT-3对该方法进行了评估，结果表明其实现了强大的零样本性能，甚至超越了在完整训练数据集上训练的部分强序列推荐模型。这些令人鼓舞的结果凸显了将LLMs用作推荐器的广阔研究前景。代码请参见https://github.com/AGI-Edgerunners/LLM-Next-Item-Rec。