结合异构关系增强图神经网络的社交推荐

Social recommendation by enhanced GNN with heterogeneous relationship

社交推荐旨在利用用户的社会属性推荐潜在的感兴趣项目,有效缓解了数据稀疏性和冷启动问题。然而现有的社交推荐算法主要面向单一社交关系进行研究,社会属性难以充分参与计算,存在未能合理利用社会异构关系和节点特征表示质量不高的问题,为此提出一种结合异构关系增强图神经网络的社交推荐模型(HR-GNN)。HR-GNN利用图卷积网络(GCN)聚合用户和项目节点信息,生成查询嵌入以查询节点信息;通过将抽样概率与邻居节点之间的一致性分数相结合的邻居抽样策略挖掘社会异构关系;用自注意力机制聚合节点信息以提高用户和项目特征表示的质量。在两个真实数据集上进行的实验结果表明,所提算法在平均绝对误差(MAE)和均方根误差(RMSE)两个指标上相较于基准算法均有明显改进,在Ciao数据集上它们分别至少降低了1.80%和1.35%,在Epinions数据集上则分别至少降低了2.80%和3.18%,验证了HR-GNN的有效性。

Social recommendation aims to use users’social attributes to recommend potential items of interest,which effectively alleviates the problems of data sparsity and cold start.However,the existing social recommendation algorithms mainly focus on studying a single social relationship,and social attributes are difficult to fully participate in calculations,so that there are problems of failure to fully explore social heterogeneous relationships and poor quality of node feature representation.Therefore,an enhanced GNN model for social recommendation with Heterogeneous Relationship(HR-GNN)was proposed.In HR-GNN,Graph Convolutional Network(GCN)was used to aggregate user and item node information to generate query embeddings for node information query;the social heterogeneity relationships were explored by neighbor sampling strategy that combines sampling probabilities with consistency scores among neighbor nodes;and the node information was aggregated by self-attention mechanism to improve the quality of user and item feature representation.Experimental results on two real-world datasets demonstrate that in comparison with baseline algorithms,the proposed algorithm has significant improvements in both Mean Absolute Error(MAE)and Root Mean Square Error(RMSE),and they are reduced by at least 1.80%and 1.35%on Ciao dataset and at least 2.80%and 3.18%on Epinions dataset,verifying the effectiveness of HR-GNN model.