Prompting is central to interaction with AI systems, yet many users struggle to explore alternative directions, articulate creative intent, or understand how variations in prompts shape model outputs. We introduce prompt recommender systems (PRS) as an interaction approach that supports exploration, suggesting contextually relevant follow-up prompts. We present PromptHelper, a PRS prototype integrated into an AI chatbot that surfaces semantically diverse prompt suggestions while users work on real writing tasks. We evaluate PromptHelper in a 2x2 fully within-subjects study (N=32) across creative and academic writing tasks. Results show that PromptHelper significantly increases users' perceived exploration and expressiveness without increasing cognitive workload. Qualitative findings illustrate how prompt recommendations help users branch into new directions, overcome uncertainty about what to ask next, and better articulate their intent. We discuss implications for designing AI interfaces that scaffold exploratory interaction while preserving user agency, and release open-source resources to support research on prompt recommendation.

翻译：提示是AI系统交互的核心，然而许多用户难以探索替代方向、表达创造性意图或理解提示的变体如何影响模型输出。我们引入提示推荐系统作为一种支持探索的交互方法，它能根据上下文推荐相关的后续提示。我们提出了PromptHelper，这是一个集成于AI聊天机器人中的PRS原型，可在用户处理实际写作任务时提供语义多样化的提示建议。我们通过2x2完全被试内设计（N=32）在创意写作与学术写作任务中对PromptHelper进行评估。结果表明，PromptHelper能显著提升用户的感知探索性和表达力，且不增加认知负荷。定性研究发现提示推荐如何帮助用户拓展新方向、克服对后续提问的不确定性，并更清晰地表达意图。我们讨论了在保持用户自主权的前提下设计支持探索性交互的AI界面的意义，并开源相关资源以支持提示推荐研究。