融合语义信息与注意力的图神经网络推荐算法

基于图神经网络的推荐算法通过从图中获取知识生成节点的特征表示,提高了推荐结果的可解释性.然而,随着推荐系统原始数据规模的不断扩大,大量包含语义信息的文本数据没有得到有效利用.同时图神经网络在融合图中邻居信息时没有区分关键节点,使得模型难以学习到高质量的实体特征,进而导致推荐质量下降.本文将图神经网络与语义模型相结合,提出一种融合语义信息与注意力的图神经网络推荐算法.该算法基于SpanBERT语义模型处理实体相关的文本信息,生成包含语义信息的特征嵌入,并将注意力机制引入到基于用户社交关系以及用户-项目交互的影响传播融合过程中,从而实现用户和项目两类实体特征的有效更新.在公开数据集上的对比实验结果表明,本文所提出的方法较现有基准方法在各项指标上均有所提升.

Graph Neural Network Recommendation Algorithm Fused with Semantic Information and Attention

The recommendation algorithm based on the graph neural network generates the feature representation of nodes by obtaining knowledge from graphs, which improves the interpretability of recommendation results. Nevertheless, as the original data scale of the recommendation system continually expands, a large amount of text data containing semantic information has not been effectively used. Additionally, the graph neural network does not distinguish key nodes when fusing the neighbor information in the graph, making it difficult for the model to learn high-quality entity features, which in turn leads to a decrease in the quality of recommendation. This study combines the graph neural network with a semantic model and proposes a recommendation algorithm based on the graph neural network which integrates semantic information and attention. This algorithm processes entity-related text information based on the SpanBERT semantic model and generates feature embeddings containing semantic information. It also introduces the attention mechanism into the process of influence propagation and fusion based on user social relations and user-item interactions to effectively update user and item entity features. The comparative experimental results on public datasets show that the proposed method is better than the existing benchmark methods in all indicators.