Task-Aware Meta-Learning on Heterogeneous Knowledge Graph for POI Recommendation
Jingyuan Wang, Zhichun Wang, Tong Lu, Yiming Guan
Abstract
Point-of-Interest (POI) recommendation plays a pivotal role in location-based services by guiding users to discover new and relevant places. While graph-based methods have shown promising results, effectively modeling the diversity and dynamics of user preferences remains a key challenge. Addressing this requires richer representations of both POIs and user interests, as well as more adaptive learning strategies. In this work, we propose TMHKG, a Task-aware Meta-learning framework with a Heterogeneous Knowledge Graph for POI recommendation. To enhance representation learning, TMHKG constructs a dual-view POI knowledge graph that integrates geographical proximity and user-aware category transitions, and models users
BibTeX
@inproceedings{aaai2026_taskawaremetalea,
title = {Task-Aware Meta-Learning on Heterogeneous Knowledge Graph for POI Recommendation},
author = {Jingyuan Wang and Zhichun Wang and Tong Lu and Yiming Guan},
booktitle = {AAAI 2026},
year = {2026}
}