图神经网络在基于知识图嵌入的推荐算法中面临数据稀疏和模型过平滑问题,而导致推荐系统难以捕捉用户潜在的长期偏好.针对该问题,提出了一种基于深度知识图卷积网络的推荐算法.首先,在利用历史交互数据构建知识图谱的基础上,通过随机采样过程来获得知识图谱的多个视图以缓解交互数据稀疏的问题.其次,在知识图卷积操作过程中引入残差连接以缓解过平滑问题,从而构建一种深度知识图卷积网络以捕获高阶邻居信息并获得用户潜在的远程兴趣.同时,为了避免深层图卷积网络带来噪声输入问题,利用图自注意力机制对深层特征表示进行提纯.为验证模型的可行性和有效性,在 3 个真实数据集上进行了大量实验.实验结果表明,所提出的模型能够有效缓解过度平滑问题.在这 3 个数据集中,模型的ROC曲线下的面积(Area Under Curve,AUC,记为AUC)分别达到了 0.974、0.812 和 0.732,相较于现有方法,模型的推荐准确度明显提升.
Recommendation algorithm based on deep knowledge graph convolution networks
Graph neural networks face the challenge of data sparsity and model over-smoothing problem in knowledge graph-based recommendation algorithms,making it difficult for recommendation systems to capture users'long-term preferences.To address this issue,a recommendation algorithm based on deep knowledge graph convolutional networks is proposed.Firstly,multiple views of the knowledge graph are obtained through a random sampling process based on historical interaction data to alleviate the problem of sparse interaction data.Secondly,residual connections are introduced in the knowledge graph convolutional operation to alleviate the over-smoothing problem,thus constructing a deep knowledge graph convolutional network to capture high-order neighbor information and obtain users'potential remote interests.To avoid noisy input brought by the deep graph convolutional network,the graph self-attention mechanism is used to refine the deep feature representations.To verify the feasibility and effectiveness of the model,extensive experiments were conducted on three real-world datasets,and the results showed that the proposed model can effectively alleviate the over-smoothing problem.In these three datasets,the model achieved Area Under Curve(AUC,AUC)scores of 0.974,0.812 and 0.732 respectively.Compared to existing methods,the model showed significant improvements in recommendation accuracy.
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