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All Journal Journal of Applied Data Sciences
Jiang, Guisong
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Journal : Journal of Applied Data Sciences

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Policy Optimization Recommendation Algorithm Based on Mapping Network for Behavior Enhancement Shan, Linlin; Jiang, Guisong; Li, Shuang; Zhao, Shuai; Luo, Kunjie; Zhang, Long; Li, Yi
Journal of Applied Data Sciences Vol 3, No 3: SEPTEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i3.67

Abstract

The algorithm of policy optimization with learning behavior enhancement based on mapping network technology was proposed, aiming to address the issues of lack and sparsity of learning behavior data and weak generalization ability of the model in AI education. Based on the basic recommendation algorithm and the framework of rein- forcement learning, and model introduces the correlation mapping network to realize the transformation of strong and weak correlation, so as to optimize the input agent policy to improve the performance model of course recommendation. Experiment on MOOC da- tasets show that the proposed algorithm model has a stable improvement compared with the baseline models, and can effectively improve the accuracy of course recommendation.
Co-Authors Luo, Kunjie Shan, Linlin Shuai Zhao, Shuai Shuang Li Yi Li Zhang, Long