基于自注意力机制的局部与全局特征融合的评分预测算法

为了完全挖掘异质信息网络中节点的特征并且更好地融合这些特征，提高推荐算法的性能，提出一种基于自注意力机制的局部与全局特征融合的评分预测算法（rating prediction algorithm based on self-attention mechanism and fusion of local & global features，AMFL&GRec）。首先基于LeaderRank算法提取目标节点的全局序列，基于元路径带偏置的随机游走算法提取节点的局部序列，通过skip-gram模型分别学习节点的全局特征与局部特征；通过自注意力机制学习目标节点对局部与全局特征的偏好，从而得到在单一元路径下节点的特征表示；再通过自注意力机制融合不同元路径下同一节点的表示，从而得到节点在不同元路径下的最终的特征表示；最后基于多层感知器实现评分预测任务。在两个真实数据集进行了大量实验，实验结果验证了AMFL&GRec算法不仅能够捕获具有密集连通节点的微观（局部）结构，而且还能够捕获该节点在网络中的全局结构，从而使其得到的节点特征得以体现节点的整体（局部+全局）特征。同时，实验结果也证明了AMFL&GRec算法评分预测性能优于对比算法，从而证明利用自注意力机制考虑异质信息网络中节点对于局部、全局特征以及元路径的偏好能够提高评分预测的准确性。

Rating prediction algorithm based on self-attention mechanism and fusion of local & global features

In order to fully mine nodes'' features and better integrate these features simultaneously in the heterogeneous information network, this paper proposed a AMFL&GRec. Firstly, AMFL&GRec used the LeaderRank algorithm to extract the target node'' global sequence, and used a meta-path-based heterogeneous information network embedding model to extract the node'' local sequence, and used the skip-gram model to learn the node'' global and local features. And then it used the self-attention mechanism to learn the preference of the target nodes'' local and global features to obtain the feature representation of the target node in a single meta-path. Secondly, it used the self-attention mechanism to fuse the representation of the same node under different meta-paths to obtain the final feature representation. Finally, it utilized a multi-layer perceptron to achieve the task of rating prediction. This paper conducted a large number of experiments on two real datasets. The experimental results verify that the AMFL&GRec algorithm can not only capture the micro(local) structure of densely connected nodes, but also capture the global structure of the node in the network, and finally obtain nodes'' overall(local+global) characteristics. At the same time, the experimental results also prove that the AMFL&GRec''s rating prediction performance is better than the baselines. It proves that in the heterogeneous information network utilizing the self-attention mechanism to consider the nodes'' preferences for local and global features and meta-paths can improve the accuracy of rating prediction.