Journal of Applied Data Sciences
Vol 3, No 3: SEPTEMBER 2022

Policy Optimization Recommendation Algorithm Based on Mapping Network for Behavior Enhancement

Shan, Linlin (Unknown)
Jiang, Guisong (Unknown)
Li, Shuang (Unknown)
Zhao, Shuai (Unknown)
Luo, Kunjie (Unknown)
Zhang, Long (Unknown)
Li, Yi (Unknown)

Article Info

Publish Date
30 Sep 2022

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.

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Journal Info
Journal of Applied Data Sciences
Website

Abbrev

JADS

Publisher

Bright Publisher

Subject

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Description

One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes ...