Flexible Keyword-Aware Top-$k$ Route Search

TL;DR

This paper introduces KATR, a flexible route search method that considers user preferences and keywords, improving route planning accuracy and efficiency over existing approaches.

Contribution

The paper proposes a novel explore-and-bound paradigm for efficient top-$k$ route search considering user preferences and keywords.

Findings

Our method outperforms existing approaches in efficiency and accuracy.

KATR effectively handles flexible user preferences and large POI datasets.

Extensive experiments validate the superiority of our approach.

Abstract

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.