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.

翻译：叙事驱动推荐（NDR）提出了一种信息获取问题，即用户通过详细描述其偏好和上下文来寻求推荐，例如旅行者在描述其喜好/厌恶及旅行情景时寻求兴趣点推荐。随着基于自然语言的搜索和推荐系统对话界面的兴起，此类请求日益重要。然而，NDR缺乏充足的模型训练数据，当前平台普遍无法支持这类请求。幸运的是，经典的用户-物品交互数据集包含丰富的文本数据（如评论），这些数据通常描述了用户偏好和上下文——可用于引导NDR模型的训练。本研究探索利用大语言模型（LLM）进行数据增强以训练NDR模型。我们采用LLM通过少样本提示从用户-物品交互中生成合成叙事查询，并基于合成查询与用户-物品交互数据训练NDR检索模型。实验表明，该策略能有效训练小参数检索模型，其性能在叙事驱动推荐中超越其他检索模型及LLM基线方法。