Recent years have witnessed the success of deep neural networks in many research areas. The fundamental idea behind the design of most neural networks is to learn similarity patterns from data for prediction and inference, which lacks the ability of cognitive reasoning. However, the concrete ability of reasoning is critical to many theoretical and practical problems. On the other hand, traditional symbolic reasoning methods do well in making logical inference, but they are mostly hard rule-based reasoning, which limits their generalization ability to different tasks since difference tasks may require different rules. Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs. In this paper, we propose Logic-Integrated Neural Network (LINN) to integrate the power of deep learning and logic reasoning. LINN is a dynamic neural architecture that builds the computational graph according to input logical expressions. It learns basic logical operations such as AND, OR, NOT as neural modules, and conducts propositional logical reasoning through the network for inference. Experiments on theoretical task show that LINN achieves significant performance on solving logical equations and variables. Furthermore, we test our approach on the practical task of recommendation by formulating the task into a logical inference problem. Experiments show that LINN significantly outperforms state-of-the-art recommendation models in Top-K recommendation, which verifies the potential of LINN in practice.

中文翻译：

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神经逻辑推理

近年来，深度神经网络在许多研究领域取得了成功。大多数神经网络设计背后的基本思想是从数据中学习相似模式进行预测和推理，缺乏认知推理能力。然而，推理的具体能力对于许多理论和实践问题至关重要。另一方面，传统的符号推理方法在进行逻辑推理方面做得很好，但它们大多是基于硬规则的推理，这限制了它们对不同任务的泛化能力，因为不同的任务可能需要不同的规则。推理和泛化能力对于推荐系统等预测任务都很重要，其中推理提供了用户历史和目标项目之间的强联系，以实现准确预测，泛化有助于模型在嘈杂的输入上绘制鲁棒的用户画像。在本文中，我们提出了逻辑集成神经网络 (LINN) 来整合深度学习和逻辑推理的力量。LINN 是一种动态神经架构，可根据输入的逻辑表达式构建计算图。它将AND、OR、NOT等基本逻辑运算作为神经模块学习，通过网络进行命题逻辑推理进行推理。理论任务实验表明，LINN 在求解逻辑方程和变量方面取得了显着的性能。此外，我们通过将任务制定为逻辑推理问题来测试我们在推荐的实际任务上的方法。实验表明，LINN 在 Top-K 推荐中明显优于最先进的推荐模型，