融合交互意图的图神经网络协同过滤算法

传统的协同过滤方法很大程度上以一种统一的方式进行建模，未从交互意图的细粒度上考虑建模关系。为此提出一种融合交互意图的图神经网络协同过滤算法（INTNGCF）。首先，通过将边分解成多个潜在空间来识别潜在意图；其次，利用交互意图融合层确定这些潜在意图的重要性；最后，生成用户对项目的预测评分。在三个真实数据集上与七种基线模型进行对比实验，结果表明，提出的算法与其他先进的推荐模型相比具有一定的性能优势。

Neural graph collaborative filtering based on interaction intent

Traditional collaborative filtering methods largely model in a uniform way without considering the modeling relationships at the fine-grained level of interaction intent. This paper proposed a graph neural network collaborative filtering algorithm fused with interaction intention, named INTNGCF. Firstly, it identified the potential intention by decomposing edges into multiple potential spaces. Secondly, it used the interaction intention fusion layer to determine the importance of these potential intentions. Finally, it generated a predicted user rating of the project. Compared with seven baseline models on three real datasets, the results show that the proposed algorithm has certain performance advantages compared with other advanced recommendation models.