With the rise of Large Language Models (LLMs), tourists increasingly use it for route planning by entering keywords for attractions, instead of relying on traditional manual map services. LLMs provide generally reasonable suggestions, but often fail to generate optimal plans that account for detailed user requirements, given the vast number of potential POIs and possible routes based on POI combinations within a real-world road network. In this case, a route-planning API could serve as an external tool, accepting a sequence of keywords and returning the top-$k$ best routes tailored to user requests. To address this need, this paper introduces the Keyword-Aware Top-$k$ Routes (KATR) query that provides a more flexible and comprehensive semantic to route planning that caters to various user's preferences including flexible POI visiting order, flexible travel distance budget, and personalized POI ratings. Subsequently, we propose an explore-and-bound paradigm to efficiently process KATR queries by eliminating redundant candidates based on estimated score bounds from global to local levels. Extensive experiments demonstrate our approach's superior performance over existing methods across different scenarios.

翻译：随着大型语言模型（LLM）的兴起，游客越来越多地通过输入景点关键词来使用其进行路线规划，而非依赖传统的人工地图服务。LLM通常能提供合理的建议，但考虑到现实道路网络中大量潜在的兴趣点（POI）以及基于POI组合的可能路线，它们往往难以生成考虑用户详细需求的最优方案。在这种情况下，路线规划API可作为外部工具，接受一系列关键词并返回符合用户请求的前$k$条最佳路线。为满足这一需求，本文提出了面向关键词的Top-$k$路线（KATR）查询，该查询为路线规划提供了更灵活、更全面的语义，能够适应包括灵活的兴趣点访问顺序、灵活的旅行距离预算以及个性化兴趣点评分在内的多种用户偏好。随后，我们提出了一种探索-定界范式，通过从全局到局部层面基于估计分数界限消除冗余候选路线，从而高效处理KATR查询。大量实验表明，在不同场景下，我们的方法相较于现有方法均表现出更优越的性能。