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

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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

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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 (No. 42371478)

Accepted Date: 2026-03-16
Rev Recd Date: 2026-03-16

Available Online: 2026-03-31

Abstract

Abstract

Objective In recent years, next point of interest (POI) recommendation has played an increasingly significant role in location-based social networks (LBSNs). However, existing graph representation learning–based recommendation methods struggle to effectively balance node distributions across different domains (i.e., node types) and often overlook the feature discrepancies among heterogeneous relations. As a result, they fail to fully exploit the complex semantic dependencies among contextual information when capturing users’ temporal preference patterns. Methods To address the above issues, we propose a next POI recommendation method based on semantic relation–enhanced adaptive graph representation learning (SR-GRL). A heterogeneous transition graph is constructed to integrate three entity types—POIs, POI categories, and regions—and their complex interrelationships. An adaptive balanced random walk sampling strategy is designed to dynamically balance node distributions across different domains and alleviate information redundancy. A type-aware attention mechanism is then employed to construct a relation transition matrix from the sampled node sequences, enabling the model to capture semantic associations among nodes and effectively distinguish feature discrepancies across diverse node types. The obtained disentangled POI representations are used to perform spatiotemporal encoding of user check-in sequences, and a self-attention mechanism aggregates users’ temporal preference features. Finally, the next POI recommendation is produced via a Softmax function. Results and Discussions Experiments on the Foursquare datasets (Tokyo and New York) and the Sina Weibo dataset (Shanghai) show that, compared with state-of-the-art baselines, SR-GRL method achieves Recall@10 improvements of 2.22%～24.16%, F1@10 improvements of 1.16%～10.48%, and NDCG@10 improvements of 3.01%～17.37%, demonstrating superior recommendation performance. Conclusions Overall, the semantic relation–enhanced adaptive graph representation learning approach can dynamically balance the distributions of different node types and substantially strengthens the modeling of complex semantic dependencies among heterogeneous contextual information.
- next point of interest recommendation,
- graph representation learning,
- random walk,
- attention mechanism,
- temporal preference

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References(21)

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