A recommendation system assists users in finding items that are relevant to them. Existing recommendation models are primarily based on predicting relationships between users and items and use complex matching models or incorporate extensive external information to capture association patterns in data. However, recommendation is not only a problem of inductive statistics using data; it is also a cognitive task of reasoning decisions based on knowledge extracted from information. Hence, a logic system could naturally be incorporated for the reasoning in a recommendation task. However, although hard-rule approaches based on logic systems can provide powerful reasoning ability, they struggle to cope with inconsistent and incomplete knowledge in real-world tasks, especially for complex tasks such as recommendation. Therefore, in this paper, we propose a neuro-symbolic recommendation model, which transforms the user history interactions into a logic expression and then transforms the recommendation prediction into a query task based on this logic expression. The logic expressions are then computed based on the modular logic operations of the neural network. We also construct an implicit logic encoder to reasonably reduce the complexity of the logic computation. Finally, a user's interest items can be queried in the vector space based on the computation results. Experiments on three well-known datasets verified that our method performs better compared to state of the art shallow, deep, session, and reasoning models.

翻译：推荐系统帮助用户找到与其相关的物品。现有推荐模型主要基于预测用户与物品之间的关系，并采用复杂匹配模型或融入大量外部信息来捕捉数据中的关联模式。然而，推荐不仅是一个利用数据进行归纳统计的问题，也是一个基于从信息中提取的知识进行推理决策的认知任务。因此，自然可以将逻辑系统融入推荐任务中进行推理。然而，虽然基于逻辑系统的硬规则方法能够提供强大的推理能力，但它们难以应对现实任务中不一致和不完备的知识，尤其是在复杂任务（如推荐）中表现不佳。为此，本文提出了一种神经符号推荐模型，该模型将用户历史交互转化为逻辑表达式，随后将推荐预测转化为基于该逻辑表达式的查询任务。这些逻辑表达式通过神经网络的模块化逻辑运算进行计算。我们还构建了一个隐式逻辑编码器，以合理降低逻辑计算的复杂度。最终，基于计算结果可在向量空间中查询用户的兴趣物品。在三个知名数据集上的实验验证表明，与最先进的浅层、深层、会话及推理模型相比，我们的方法表现更优。