[1]张 宇,程 玉,陈建峡,等. 基于深度学习的在线推荐学习系统设计与开发[J].湖北工业大学学报,2021,(5):64-69.
　ZHANG Yu,CHENG Yu,CHEN Jianxia,et al. Design and Development of Online Recommendation Learning System Based on Deep Learning[J].,2021,(5):64-69.
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 基于深度学习的在线推荐学习系统设计与开发()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
期数:
2021年第5期
页码:
64-69
栏目:
湖北工业大学学报
出版日期:
2021-10-31

文章信息/Info

Title:
 Design and Development of Online Recommendation Learning System Based on Deep Learning
文章编号:
1003-4684(2021)05-0064-06
作者:
 张 宇;  程 玉; 陈建峡;  周世星;  高 奕
 湖北工业大学计算机学院， 湖北 武汉 430068
Author(s):
 ZHANG Yu;  CHENG Yu;  CHEN Jianxia;  ZHOU Shixing;  GAO Yi
 School of Computer Science, Hubei Univ. of Tech., Wuhan 430068, China
关键词:
 ; 深度学习;  推荐系统;  评分预测;  在线学习
Keywords:
 ; deep learning;  recommendation system;  scoring prediction;  online learning
分类号:
TP183
文献标志码:
A
摘要:
 为满足用户对资源检索越来越高的要求，基于深度学习的在线推荐系统, 研发基于神经网络的评分预测模型。该模型在词嵌入和文本卷积网络的基础上融合了深度学习模型,可以挖掘出用户隐藏的兴趣特征。实验研究结果证明,该在线网络学习服务平台不仅能够有效满足高校学生的多种多样个性化学习需求,还能提高师生学习工作效率。
Abstract:
 With the development of online learning systems, users require resource retrieval abilities to be improved greatly. This paper proposes a novel online recommendation system based on deep learning, which can implement the main functions of online learning, online programming and online communication. In particular, this paper develops a rating prediction model based on neural network, which integrates deep learning model on the basis of word embedding and text convolution network, and it can mine the hidden interest features of users. The experimental results fully show that the proposed platform can not only effectively satisfy the various personalized learning requirements of college students, but also improve the learning efficiency of teachers and students.

参考文献/References:

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备注/Memo

备注/Memo:
 ［收稿日期］ 2021-06-04
［第一作者］ 张 宇(2001-)， 男， 湖北武汉人，湖北工业大学本科生，研究方向为软件工程
［通信作者］ 程 玉(1969-)， 女， 河南信阳人，湖北工业大学副教授，研究方向为数据库技术
更新日期/Last Update: 2021-11-01