Sequential Recommendation with Probabilistic Logical Reasoning,arXiv |
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Sequential Recommendation with Probabilistic Logical Reasoning,arXiv
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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.
中文翻译：
深度学习和符号学习是顺序推荐 (SR) 中常用的两种方法。最近的神经符号 SR 模型展示了它们使 SR 具备并发感知和认知能力的潜力。然而，由于在逻辑推理中表示用户和项目等开放性问题，神经符号 SR 仍然是一个具有挑战性的问题。在本文中，我们将深度神经网络 (DNN) SR 模型与逻辑推理相结合，并提出了一个名为 Sequential Recommendation with Probabilistic Logical Reasoning（SR-PLR 的缩写）的通用框架。该框架允许 SR-PLR 通过分离 DNN 和概率逻辑网络中的特征嵌入和逻辑嵌入来受益于相似性匹配和逻辑推理。为了更好地捕捉用户口味的不确定性和演变，SR-PLR以概率方法嵌入用户和物品，对用户的交互模式进行概率逻辑推理。然后将从 DNN 和逻辑网络中学习到的特征和逻辑表示连接起来进行预测。最后，各种顺序推荐模型的实验证明了 SR-PLR 的有效性。 |
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