Global and local hypergraph learning method with semantic enhancement for POI recommendation

The deep semantic information mining extracts deep semantic features from textual data and effectively utilizes the world knowledge embedded in these features, so it is widely researched in recommendation tasks. In spite of the extensive utilization of contextual information in prior Point-of-Interest research, the insufficient and non-informative textual content has led to the neglect of deep semantic study. Besides, effectively integrating the deep semantic information into the trajectory modeling process is also an open question for further exploration. Therefore, this paper proposes HyperSE, to leverage prompt engineering and pre-trained language models for deep semantic enhancement. Besides, HyperSE effectively extracts higher-order collaborative signals from global and local hypergraphs, seamlessly integrating topological and semantic information to enhance trajectory modeling. Experimental results show that HyperSE outperforms the strong baseline, demonstrating the effectiveness of the deep semantic information and the model’s efficiency. •Proposes a novel global and local hypergraph learning framework for POI recommendation Effectively captures long-term and short-term higher-order collaborative preferences.•Develops a deep semantic enhancement method exploiting pre-trained language models.•Fuses topological and semantic representations for user preference modeling.•Outperforms strong baselines through extensive experiments.•Provides new insights into recommendation system optimization.