Research Article
Design of Multimedia Learning Resource Recommendation System Based on Recurrent Neural Network
@INPROCEEDINGS{10.1007/978-3-030-82565-2_20, author={Zijin Xiao and Ying Li and Hai Zhou}, title={Design of Multimedia Learning Resource Recommendation System Based on Recurrent Neural Network}, proceedings={Multimedia Technology and Enhanced Learning. Third EAI International Conference, ICMTEL 2021, Virtual Event, April 8--9, 2021, Proceedings, Part II}, proceedings_a={ICMTEL PART 2}, year={2021}, month={7}, keywords={Recurrent neural network Multimedia learning resources Resource recommendation Demand retrieval Learner preference}, doi={10.1007/978-3-030-82565-2_20} }- Zijin Xiao
Ying Li
Hai Zhou
Year: 2021
Design of Multimedia Learning Resource Recommendation System Based on Recurrent Neural Network
ICMTEL PART 2
Springer
DOI: 10.1007/978-3-030-82565-2_20
Abstract
The existing learning resource recommendation system has the defect that the average absolute error of the recommendation result is large due to the limitation of its own adaptation range. For this reason, this research designed a multimedia learning resource recommendation system based on recurrent neural network. Introduce the recurrent neural network to design the architecture of the multimedia learning resource recommendation system, and design the system functional modules based on this, including the learner’s demand retrieval representation module, learner preference representation module, recurrent neural network training module, database module and system management module. The simulation experiment results show that compared with the existing system, under the Gowalla data set, the average absolute error coefficient of the recommended results of this paper is reduced by 0.356; under the Yelp data set, the average absolute error coefficient of the recommended results of this paper is reduced 0.404. The above results fully show that the recommendation effect of this system is better.
