Semantic Relation-Enhanced Adaptive Graph Representation Learning for Next POI Recommendation
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摘要: 针对现有基于图表示学习的下一个兴趣点推荐方法无法有效平衡不同域的节点分布、未充分考虑异质关系之间的特征差异等问题，提出了基于语义关系增强的自适应图表示学习下一个兴趣点推荐方法(SR-GRL)。该方法利用POI、POI类别、区域三种类型实体构建异构转移图，设计自适应平衡随机游走算法进行跨域节点采样，通过跳跃和停留动态平衡不同域上的节点分布，避免采样序列偏向于局部结构。然后建立类型感知注意力机制捕捉不同类型节点之间的语义关联关系形成POI解耦表示，有效区分了不同类型节点间的特征差异。在此基础上，结合自注意力机制聚合用户行为的时序偏好特征，通过Softmax函数实现下一个POI推荐。三个真实数据集上的对比实验结果表明，SR-GRL方法相比于其他对比方法具有更好的推荐性能。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. -
表 1 数据集统计
数据集名称 用户数 POI数 区域数 POI类别数 签到总数 TKY 2292 11,333 642 211 368,256 NYC 1056 8140 1257 228 142,622 新浪微博 1951 1508 516 134 279,761 
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表 2 超参数设置
数据集名称 序列最大长度 学习率 批量大小 dropout值 迭代次数 注意力头数 负样本率 TKY 50 0.003 128 0.4 100 6 6 NYC 50 0.003 256 0.4 100 8 6 新浪微博 70 0.005 256 0.5 150 8 8
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