  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.
