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，尽管技术持续进步，每遇到新的应用领域，我们仍需从头训练神经协同过滤模型。相比之下，预训练的视觉和语言模型可直接应用于多种任务（零样本场景）或仅需少量微调。受预训练模型影响力的启发，我们探索了构建预训练推荐器模型的可能性——该模型能够以最少或零重训练方式支持新领域推荐系统的开发，且无需使用任何辅助用户或物品信息。在无辅助信息条件下实现零样本推荐颇具挑战，因为当数据集间不存在重叠用户或物品时，我们无法建立跨数据集的用户-物品关联。我们的核心洞见在于：用户-物品交互矩阵的统计特征具有跨领域和数据集的普适性。因此，我们利用用户-物品交互矩阵的统计特征来识别与数据集无关的用户和物品表示。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表示（即无需用户或物品辅助信息即可支持零样本适配）。通过挖掘交互数据的统计特性（包括用户与物品边际分布，以及其聚类的规模与密度分布）来学习这些表示。