Sequential Recommendation with Probabilistic Logical Reasoning
This work addresses the problem of integrating logical reasoning into sequential recommendation systems for better user modeling, though it appears incremental as it builds on existing neural-symbolic approaches.
The paper tackles the challenge of neural-symbolic sequential recommendation by proposing SR-PLR, a framework that combines deep neural networks with probabilistic logical reasoning to enhance user and item representation, resulting in improved performance across various models as demonstrated in experiments.
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.