Deep learning and symbolic learning are two frequently employed methods in Sequential Recommendation (SR). Recent neural-symbolic SR models demonstrate their potential to enable SR to be equipped with concurrent perception and cognition capacities. However, neural-symbolic SR remains a challenging problem due to open issues like representing users and items in logical reasoning. In this paper, we combine the Deep Neural Network (DNN) SR models with logical reasoning and propose a general framework named Sequential Recommendation with Probabilistic Logical Reasoning (short for SR-PLR). This framework allows SR-PLR to benefit from both similarity matching and logical reasoning by disentangling feature embedding and logic embedding in the DNN and probabilistic logic network. To better capture the uncertainty and evolution of user tastes, SR-PLR embeds users and items with a probabilistic method and conducts probabilistic logical reasoning on users' interaction patterns. Then the feature and logic representations learned from the DNN and logic network are concatenated to make the prediction. Finally, experiments on various sequential recommendation models demonstrate the effectiveness of the SR-PLR.

翻译：深度学习与符号学习是序列推荐（SR）中两种常用的方法。近期神经符号SR模型展现了其赋予SR同时具备感知与认知能力的潜力。然而，由于在逻辑推理中表示用户和物品等未解决问题，神经符号SR仍是一个具有挑战性的问题。本文我们将深度神经网络（DNN）SR模型与逻辑推理相结合，提出一个通用框架——基于概率逻辑推理的序列推荐（简称SR-PLR）。该框架通过解耦DNN和概率逻辑网络中的特征嵌入与逻辑嵌入，使SR-PLR既能受益于相似度匹配，又能受益于逻辑推理。为了更好地捕捉用户偏好的不确定性与演变，SR-PLR采用概率方法嵌入用户和物品，并对用户交互模式进行概率逻辑推理。随后，将DNN和逻辑网络学习到的特征表示与逻辑表示进行拼接以进行预测。最后，在多种序列推荐模型上的实验证明了SR-PLR的有效性。