The recommendation system is not only a problem of inductive statistics from data but also a cognitive task that requires reasoning ability. The most advanced graph neural networks have been widely used in recommendation systems because they can capture implicit structured information from graph-structured data. However, like most neural network algorithms, they only learn matching patterns from a perception perspective. Some researchers use user behavior for logic reasoning to achieve recommendation prediction from the perspective of cognitive reasoning, but this kind of reasoning is a local one and ignores implicit information on a global scale. In this work, we combine the advantages of graph neural networks and propositional logic operations to construct a neuro-symbolic recommendation model with both global implicit reasoning ability and local explicit logic reasoning ability. We first build an item-item graph based on the principle of adjacent interaction and use graph neural networks to capture implicit information in global data. Then we transform user behavior into propositional logic expressions to achieve recommendations from the perspective of cognitive reasoning. Extensive experiments on five public datasets show that our proposed model outperforms several state-of-the-art methods, source code is avaliable at [https://github.com/hanzo2020/GNNLR].

翻译：推荐系统不仅是一个从数据中归纳统计的问题，更是一项需要推理能力的认知任务。最先进的图神经网络因能捕捉图结构数据中的隐式结构化信息，已被广泛应用于推荐系统。然而，与大多数神经网络算法类似，它们仅从感知角度学习匹配模式。部分研究者利用用户行为进行逻辑推理，从认知推理视角实现推荐预测，但这种推理具有局部性，忽略了全局尺度的隐式信息。本研究结合图神经网络与命题逻辑运算的优势，构建了一种兼具全局隐式推理能力与局部显式逻辑推理能力的神经符号推荐模型。我们首先基于相邻交互原则构建物品-物品图，利用图神经网络捕获全局数据中的隐式信息；进而将用户行为转化为命题逻辑表达式，从认知推理视角实现推荐。在五个公开数据集上的大量实验表明，所提模型优于多种现有最先进方法，源代码已开源至 https://github.com/hanzo2020/GNNLR。