Sequential Recommendation with Probabilistic Logical Reasoning
- URL: http://arxiv.org/abs/2304.11383v2
- Date: Mon, 15 May 2023 14:39:49 GMT
- Title: Sequential Recommendation with Probabilistic Logical Reasoning
- Authors: Huanhuan Yuan, Pengpeng Zhao, Xuefeng Xian and Guanfeng Liu and Victor
S. Sheng and Lei Zhao
- Abstract summary: We combine the Deep Neural Network (DNN) SR models with logical reasoning.
This framework allows SR-PLR to benefit from both similarity matching and logical reasoning.
Experiments on various sequential recommendation models demonstrate the effectiveness of the SR-PLR.
- Score: 24.908805534534547
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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