Multi-View Contrastive Fusion POI Recommendation Based on Hypergraph Neural Network

Luyao Hu, Guangpu Han, Shichang Liu, Yuqing Ren, Xu Wang, Ya Liu, Junhao Wen () and Zhengyi Yang ()
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Luyao Hu: Chongqing Division, PetroChina Southwest Oil & Gasield Company, Chongging 400707, China
Guangpu Han: Chongqing Division, PetroChina Southwest Oil & Gasield Company, Chongging 400707, China
Shichang Liu: Chongqing Division, PetroChina Southwest Oil & Gasield Company, Chongging 400707, China
Yuqing Ren: Chongqing Division, PetroChina Southwest Oil & Gasield Company, Chongging 400707, China
Xu Wang: Chongqing Division, PetroChina Southwest Oil & Gasield Company, Chongging 400707, China
Ya Liu: Chongqing Division, PetroChina Southwest Oil & Gasield Company, Chongging 400707, China
Junhao Wen: School of Bigdata and Software Engineering, Chongqing University, Chongqing 400044, China
Zhengyi Yang: School of Bigdata and Software Engineering, Chongqing University, Chongqing 400044, China

Mathematics, 2025, vol. 13, issue 6, 1-19

Abstract: In the era of information overload, location-based social software has gained widespread popularity, and the demand for personalized POI (Point of Interest) recommendation services is growing rapidly. Recommending the next POI is crucial in recommendation systems, aiming to suggest appropriate next-visit locations based on users’ historical trajectories and check-in data. However, the existing research often neglects user preferences’ diversity and dynamic nature and the need for the deep modeling of key collaborative relationships across various dimensions. As a result, the recommendation performance is limited. To address these challenges, this paper introduces an innovative Multi-View Contrastive Fusion Hypergraph Learning Model (MVHGAT). The model first constructs three distinct hypergraphs, representing interaction, trajectory, and geographical location, capturing the complex relationships and high-order dependencies between users and POIs from different perspectives. Subsequently, a targeted hypergraph convolutional network is designed for aggregation and propagation, learning the latent factors within each view. Through multi-view weighted contrastive learning, the model uncovers key collaborative effects between views, enhancing both user and POI representations’ consistency and discriminative power. The experimental results demonstrate that MVHGAT significantly outperforms several state-of-the-art methods across three public datasets, effectively addressing issues such as data sparsity and oversmoothing. This model provides new insights and solutions for the next POI recommendation task.

Keywords: next POI recommendation; multi-view learning; hypergraph learning; contrastive learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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