融合语义增强的用户兴趣度预测方法研究

Research on User Interest Prediction Method Combined with Semantic Enhancement

基于图神经网络的社会化推荐是现有模型中性能较好的一类方法,通过挖掘图结构信息缓解数据稀疏问题.然而现有大多数模型仅考虑浅层的语义上下文信息,导致模型难以学习到高质量的用户/项目向量.为此,本文提出了一种融合语义增强的用户兴趣度预测方法.该模型通过学习用户-项目二部图中的语义关系构建语义增强的用户/物品网络,将其与社交网络送入关系感知图神经网络中进行深层上下文信息的聚合,利用多层感知机对生成的用户兴趣和物品嵌入进行拼接,最终预测用户和物品的交互得分.对Ciao和Epinions两个公开数据集进行仿真实验,实验结果显示,模型在Recall@K(召回率)和NDCG@K(归一化折损累计增益)两个方面相较于最优基线平均提升了3.55%和2.21%,从而验证在进行语义增强和上下文感知聚合后,算法的有效性得到了提升.

Social recommendation based on graph neural network are methods with better performance in existing models.It can alleviate the problem of data sparseness by mining graph structure information.However,most existing models only consider shallow semantic context information,which makes GNN difficult to learn high-quality user/item embedding representations.For this reason,this paper proposes a method for predicting user interest combined with semantic enhancement.The model builds a semantically enhanced user network and item network by learning the semantic relationship in the user-item bipartite graph,then sends them and the social network to the connection-aware graph neural network for the perception and aggregation of deep context information.The perception splices the generated user interest and item attribute embedding representations,and finally predict the interaction probability between the target user and the candidate item.A series of simulation experiments were performed on two public datasets of Ciao and Epinions.The experimental results showed that the model has an average improvement of 3.55%and 2.21%in Recall@K and NDCG@K(Normalized discounted cumulative gain)compared to the optimal baseline.It verifies that the effectiveness of the algorithm has been improved after semantic enhancement and context-aware aggregation.