Waypoint POI Recommendation for Vehicle Navigation Services using Hierarchical Graphs and Contrastive Learning

Abstract

Modern vehicle navigation systems can greatly benefit from waypoint point-of-interest (POI) recommendation, which suggests personalized intermediate stops along a driving route. This paper defines the novel waypoint POI recommendation problem: given a starting point and a destination, recommend one or more personalized POIs to visit en route. This scenario (e.g., suggesting a lunch stop during a road trip) differs from the conventional ''next POI'' recommendation in that it infers waypoint POIs from only two (origin and destination) inputs and predicts multiple intermediate stops rather than a single next location. To solve this problem, we propose WayPOI, a novel recommender model for Waypoint POI suggestion based on hierarchical graph based contrastive learning (WayPOI). WayPOI constructs a hierarchical graph that captures both individual and group-level behavioral patterns of users and POIs, and it employs a contrastive learning strategy to learn effective user and POI representations from sparse data. Through experiments on real-world driving data provided by Hyundai as well as on three public datasets, we demonstrate that WayPOI significantly outperforms several recent POI recommendation models, even though these baselines were carefully re-formed and retrained to perform waypoint recommendation for a fair comparison. Our ablation study confirms the benefit of each proposed component.