The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation systems. Though effective while user-item interactions are abundant, conventional recommendation systems struggle to recommend cold-start items without historical interactions. To address this, we propose utilizing LLMs as data augmenters to bridge the knowledge gap on cold-start items during training. We employ LLMs to infer user preferences for cold-start items based on textual description of user historical behaviors and new item descriptions. The augmented training signals are then incorporated into learning the downstream recommendation models through an auxiliary pairwise loss. Through experiments on public Amazon datasets, we demonstrate that LLMs can effectively augment the training signals for cold-start items, leading to significant improvements in cold-start item recommendation for various recommendation models.

翻译：大型语言模型（LLMs）的推理与泛化能力有助于我们更深入地理解用户偏好与物品特性，为增强推荐系统带来了令人期待的前景。尽管在用户-物品交互数据充足时效果显著，但传统推荐系统在推荐缺乏历史交互的冷启动物品时仍面临挑战。为解决这一问题，我们提出利用LLMs作为数据增强器，在训练过程中弥补冷启动物品的知识鸿沟。我们基于用户历史行为的文本描述与新物品描述，借助LLMs推断用户对冷启动物品的偏好。随后，通过辅助成对损失函数将增强后的训练信号纳入下游推荐模型的学习过程。在公开亚马逊数据集上的实验表明，LLMs能够有效增强冷启动物品的训练信号，从而显著提升多种推荐模型在冷启动物品推荐上的性能。