  Narrative-driven recommendation (NDR) presents an information access problem
where users solicit recommendations with verbose descriptions of their
preferences and context, for example, travelers soliciting recommendations for
points of interest while describing their likes/dislikes and travel
circumstances. These requests are increasingly important with the rise of
natural language-based conversational interfaces for search and recommendation
systems. However, NDR lacks abundant training data for models, and current
platforms commonly do not support these requests. Fortunately, classical
user-item interaction datasets contain rich textual data, e.g., reviews, which
often describe user preferences and context - this may be used to bootstrap
training for NDR models. In this work, we explore using large language models
(LLMs) for data augmentation to train NDR models. We use LLMs for authoring
synthetic narrative queries from user-item interactions with few-shot prompting
and train retrieval models for NDR on synthetic queries and user-item
interaction data. Our experiments demonstrate that this is an effective
strategy for training small-parameter retrieval models that outperform other
retrieval and LLM baselines for narrative-driven recommendation.
