Semantic Relation-enhanced Adaptive Graph Representation Learning for Next POI Recommendation

WANG Zhuolu; XU Shenghua; WANG Yong; JIANG Shunshun

doi:10.11999/JEIT251357

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WANG Zhuolu, XU Shenghua, WANG Yong, JIANG Shunshun. Semantic Relation-enhanced Adaptive Graph Representation Learning for Next POI Recommendation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251357

Citation:

WANG Zhuolu, XU Shenghua, WANG Yong, JIANG Shunshun. Semantic Relation-enhanced Adaptive Graph Representation Learning for Next POI Recommendation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251357

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Semantic Relation-enhanced Adaptive Graph Representation Learning for Next POI Recommendation

doi: 10.11999/JEIT251357 cstr: 32379.14.JEIT251357

1.
School of Geomatics, Liaoning Technical University Fuxin 123000, China
2.
Chinese Academy of Surveying and Mapping Beijing 100036, China

Funds: The National Natural Science Foundation of China (42371478)

Received Date: 2025-12-24
Accepted Date: 2026-03-16
Rev Recd Date: 2026-03-13

Available Online: 2026-03-31

Abstract

Abstract

Objective In recent years, next Point Of Interest (POI) recommendation has played an increasingly important role in Location-Based Social Networks (LBSNs). However, existing Graph Representation Learning (GRL)-based recommendation methods have struggled to balance node distributions across different domains (i.e., node types) effectively and have often overlooked feature differences among heterogeneous relations. Thus, complex semantic dependencies in contextual information cannot be fully captured when users’ temporal preference patterns are modeled. Methods To address these issues, a next POI recommendation method based on Semantic Relation-enhanced adaptive Graph Representation Learning (SR-GRL) is proposed. A heterogeneous transition graph is constructed to integrate three entity types, namely POIs, POI categories, and regions, and their complex interrelationships. An adaptive balanced random walk sampling strategy is designed to balance node distributions across different domains dynamically and to reduce information redundancy. A type-aware attention mechanism is then used to learn semantic associations among nodes through relation-specific transformation matrices, so that feature differences across node types can be identified effectively. The obtained disentangled POI representations are then used for spatiotemporal encoding of user check-in sequences, and a self-attention mechanism is applied to aggregate users^, temporal preference features. Finally, next POI recommendation is generated through a Softmax function. Results and Discussions Experiments on the Foursquare datasets from Tokyo and New York and the Sina Weibo dataset from Shanghai show that, compared with state-of-the-art baselines, the SR-GRL method achieves Recall@10 improvements of 2.22%$ \sim $24.16%, F1@10 improvements of 1.16%$ \sim $10.48%, and NDCG@10 improvements of 3.01%$ \sim $17.37%, indicating better recommendation performance. Conclusions Overall, the SR-GRL approach can balance the distributions of different node types dynamically and strengthen the modeling of complex semantic dependencies in heterogeneous contextual information.
- Next Point Of Interest(POI) recommendation,
- Graph Representation Learning(GRL),
- Random walk,
- Attention mechanism,
- Temporal preference

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References(21)

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