Modern neural collaborative filtering techniques are critical to the success of e-commerce, social media, and content-sharing platforms. However, despite technical advances -- for every new application domain, we need to train an NCF model from scratch. In contrast, pre-trained vision and language models are routinely applied to diverse applications directly (zero-shot) or with limited fine-tuning. Inspired by the impact of pre-trained models, we explore the possibility of pre-trained recommender models that support building recommender systems in new domains, with minimal or no retraining, without the use of any auxiliary user or item information. Zero-shot recommendation without auxiliary information is challenging because we cannot form associations between users and items across datasets when there are no overlapping users or items. Our fundamental insight is that the statistical characteristics of the user-item interaction matrix are universally available across different domains and datasets. Thus, we use the statistical characteristics of the user-item interaction matrix to identify dataset-independent representations for users and items. We show how to learn universal (i.e., supporting zero-shot adaptation without user or item auxiliary information) representations for nodes and edges from the bipartite user-item interaction graph. We learn representations by exploiting the statistical properties of the interaction data, including user and item marginals, and the size and density distributions of their clusters.

翻译：现代神经协同过滤技术对电子商务、社交媒体及内容共享平台的成功至关重要。然而，尽管技术不断进步，每当面对新的应用领域时，我们仍需从头训练NCF模型。相比之下，预训练的视觉和语言模型可直接（零样本）或通过少量微调应用于各类任务。受预训练模型影响力的启发，我们探索了支持在新领域构建推荐系统的预训练推荐模型的可能性——这些模型无需或仅需极少重训练，且不使用任何辅助用户或物品信息。在不依赖辅助信息的情况下实现零样本推荐极具挑战性，因为当跨数据集不存在重叠用户或物品时，我们无法建立用户与物品间的关联。我们的核心洞见在于：用户-物品交互矩阵的统计特性在不同领域和数据集中具有普适性。因此，我们利用用户-物品交互矩阵的统计特性来识别与数据集无关的用户和物品表征。我们展示了如何从二分用户-物品交互图中学习节点和边的通用（即无需用户或物品辅助信息即可实现零样本适应）表征。该表征学习过程利用了交互数据的统计属性，包括用户和物品的边缘分布，以及其聚类的规模与密度分布。