中南民族大学学报（自然科学版）2026，Vol.45Issue(2)：191-201,11.DOI:10.20056/j.cnki.ZNMDZK.20250831
自适应的邻接关系:去相关图方法在下个兴趣点推荐的应用
Adaptive adjacency relationships:Application of decorrelated graph methods in next POI recommendation
摘要
Abstract
The task of next Point-of-Interest(POI)recommendation plays a significant role in social networking services.Its goal is to predict the next POI that a user is most likely to visit,based on their historical check-in records.Existing methods generally construct relational graphs for POIs using predefined graphs,while only a few studies have explored adaptive graph approaches.Adaptive graph representation learning can capture more meaningful graph structures,enabling the subsequent propagation process in Graph Neural Networks(GNNs)to learn more significant adjacency relationships,and thus obtain more valuable POI embedding.This enables downstream sequence modeling tasks to capture potential relationships between POIs better.However,research on adaptive graphs remains in its early stages.In this paper,we propose a Decorrelated Graph Representation-enhanced Attention Network(DGRAN)for the next POI recommendation task.Additionally,this paper explores the relationship between the self-attention mechanism and adaptive graph learning,introducing extra residual connections to self-attention methods in this field to increase the gradient and ensure the hight quality updates of the adaptive graph structure.Results on two real-world datasets demonstrate that the method of this paper outperforms state-of-the-art baselines.关键词
下个兴趣点推荐/自适应图/额外残差连接Key words
next POI recommendation/adaptive graph/extra residual connections分类
信息技术与安全科学引用本文复制引用
王世杰,李艳红,徐昊翔,张法..自适应的邻接关系:去相关图方法在下个兴趣点推荐的应用[J].中南民族大学学报（自然科学版）,2026,45(2):191-201,11.基金项目
湖北省自然科学基金资助项目(2017CFB135) （2017CFB135）
中央高校基本科研业务费专项资金资助项目(CZY23019) （CZY23019）
网络创新及应用型人才课程实践教学研究资助项目(2019年第一批) （2019年第一批）
