基于注意力机制和异质信息网络元路径的推荐系统

异质信息网络（HIN）包含丰富的网络结构和语义信息使其常见于推荐系统中。然而，当前推荐系统的研究工作主要是基于元路径提供的间接信息进行推荐，而未充分利用直接交互信息。为了充分利用这些信息，提出一种融合注意力机制和异质信息网络元路径的三元交互模型（AMMRec）。在异质信息网络中使用隐式反馈矩阵构造用户相似度矩阵和项目相似度矩阵，运用异质信息网络的表示学习方法获得对应的特征向量嵌入，通过注意力机制对其进行修正；设计注意力神经网络，将不同元路径的表示向量进行融合；拼接用户嵌入、元路径嵌入和项目嵌入，通过全连接神经网络生成推荐结果。在真实数据集上的实验结果表明，AMMRec的推荐精度最高提升了9.5%。此外，AMMRec对推荐结果具有良好的可解释性。

Recommendation systems based on attention mechanism and meta-paths of heterogeneous information networks

Because heterogeneous information network(HIN) contains rich network structure and semantic information, recommendation systems often use HIN for recommendation. However, the current researches of recommender systems is mainly based on indirect information provided by meta-paths for recommendation, but these researches don''t make full use of direct interactive information. To make full use of this information, this paper proposed a ternary interaction model(AMMRec) that incorporated attention mechanisms and heterogeneous information network meta-paths. This method firstly used the implicit feedback matrix to construct user similarity matrix and item similarity matrix and used the representation learning method of the HIN to obtain the corresponding feature vector embeddings in HIN. Then it used the the attention mechanism to modify the embeddings and designed attention neural network to fuse representation vectors of different meta-paths. Finally it concatenated user embeddings and meta-path embeddings and item embeddings, and generated recommendation results through fully connected neural network. The experimental results on real datasets show that AMMRec improves the recommendation accuracy by up to 9.5%. In addition, AMMRec has good interpretability for the recommendation results.