With the continuous accumulation of massive amounts of mobile data, point-of-interest (POI) recommendation has become a vital task for location-based social networks. Deep neural networks or matrix factorization (MF) alone are challenging to effectively learn user–POI interaction functions. Moreover, the user–POI interaction matrix is sparse, and the heterogeneous characteristics of auxiliary information are underused. Therefore, we propose an innovative POI recommendation method that integrates attention-aware meta-paths based on deep neural matrix factorization (DNMF-AM). First, we develop a multi-relational heterogeneous information network of “user–POI–geographic region–POI category.” Multiple-weighted isomorphic information networks based on meta-paths are employed to obtain node-embedding vectors across different relationships. Attention networks are employed to aggregate node vectors across various relationships and serve as auxiliary information to mitigate the challenges of data sparsity. Subsequently, the internal embedding vectors of the users and POIs are extracted using feature embedding based on the user–POI interaction matrix. Second, these vectors are integrated with the embedding vectors obtained by aggregating the attention networks. Third, deep neural matrix factorization is used to learn linear and nonlinear user–POI interactions to mitigate the implicit feedback problem. This outcome is achieved using generalized matrix factorization and convolution-constrained multi-head self-attention mechanism deep neural networks. Extensive experiments conducted on two real-world datasets demonstrate that the DNMF-AM outperforms the optimal baseline NeuMF-CAA by 4.24% and 5.04% in terms of HR@10 and NDCG@10, respectively.

随着海量移动数据的不断积累，兴趣点（POI）推荐已成为基于位置的社交网络的重要任务。仅深度神经网络或矩阵分解 (MF) 很难有效学习用户与 POI 交互功能。此外，用户与POI交互矩阵稀疏，辅助信息的异构特征未被充分利用。因此，我们提出了一种创新的 POI 推荐方法，该方法集成了基于深度神经矩阵分解（DNMF-AM）的注意力感知元路径。首先，我们开发了“用户-POI-地理区域-POI类别”的多关系异构信息网络。采用基于元路径的多重加权同构信息网络来获取跨不同关系的节点嵌入向量。注意力网络用于聚合各种关系中的节点向量，并作为辅助信息来缓解数据稀疏性的挑战。随后，使用基于用户-POI 交互矩阵的特征嵌入来提取用户和 POI 的内部嵌入向量。其次，这些向量与通过聚合注意力网络获得的嵌入向量相集成。第三，深度神经矩阵分解用于学习线性和非线性用户 - POI 交互，以减轻隐式反馈问题。这一结果是使用广义矩阵分解和卷积约束多头自注意力机制深度神经网络实现的。对两个真实世界数据集进行的大量实验表明，DNMF-AM 在 HR@10 和 NDCG@10 方面分别优于最佳基线 NeuMF-CAA 4.24% 和 5.04%。