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.

通过利用用户 - 物品交互矩阵的统计特性，我们学习了用于支持无需用户或物品辅助信息的零 - shot 自适应（zero-shot recommendation）的通用表示，从而探索了预训练推荐模型在新领域构建推荐系统的可能性。

预训练神经推荐系统：一个可迁移的零样本框架

Pre-trained Neural Recommenders: A Transferable Zero-Shot Framework for  Recommendation Systems

We present a method for zero-shot recommendation of multimodal non-stationary
content that leverages recent advancements in the field of generative AI. We
propose rendering inputs of different modalities as textual descriptions and to
utilize pre-trained LLMs to obtain their numerical representations by computing
semantic embeddings. Once unified representations of all content items are
obtained, the recommendation can be performed by computing an appropriate
similarity metric between them without any additional learning. We demonstrate
our approach on a synthetic multimodal nudging environment, where the inputs
consist of tabular, textual, and visual data.

利用生成型 AI 领域的最新进展，我们提出了一种用于零样本推荐多模态非稳态内容的方法。我们将不同类型的输入呈现为文本描述，并利用预训练的 LLMs 通过计算语义嵌入来获取它们的数值表示。一旦获得了所有内容项的统一表示，可以通过计算它们之间的适当相似性度量进行推荐而无需额外的学习。我们在合成的多模态推动环境中展示了我们的方法，其中输入包括表格、文本和视觉数据。