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。