Abstract: This paper proposes a long-tail-aware POI recommendation method using a global frequency hypergraph network and a multi-span HiLo (high/low) attention mechanism. First, a global frequency hypergraph network is constructed to separately model the subsequent trajectories of head and tail POIs, thus mining higher-order frequency signals across sessions and capturing complex interaction features. Then, a HiLo attention mechanism with multiple temporal spans is used to capture high- and low-frequency features within the hidden sequences. Finally, a long-tail enhancement module is introduced to improve the propagation ability of tail POIs through adaptive edge deletion and enhanced graph construction. Experimental results on multiple real-world datasets show that the proposed method outperforms existing baseline models and demonstrates significant advantages, particularly in long-tail POI recommendation tasks.

Key words: long-tail effect, hypergraph neural network, HiLo (high/low) attention, enhanced graph, adaptive edge deletion