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浙江大学学报(工学版)  2019, Vol. 53 Issue (11): 2139-2145    DOI: 10.3785/j.issn.1008-973X.2019.11.011
计算机技术与控制工程     
基于深度学习的课程推荐模型
厉小军1(),柳虹2,*(),施寒潇1,朱柳青2,张亚辉2
1. 浙江工商大学 管理工程与电子商务学院,浙江 杭州 310018
2. 浙江工商大学 计算机与信息工程学院,浙江 杭州 310018
Deep learning based course recommendation model
Xiao-jun LI1(),Hong LIU2,*(),Han-xiao SHI1,Liu-qing ZHU2,Ya-hui ZHANG2
1. School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China
2. Computer and Information Engineering College, Zhejiang Gongshang University, Hangzhou 310018, China
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摘要:

针对网络课程推荐中数据稀疏和推荐效果不佳的问题,将深度学习引入课程推荐,提出基于辅助信息的神经网络模型(IUNeu). 该模型在已有神经矩阵分解模型(NeuMF)的基础上,结合用户信息和课程信息,并考虑它们之间的相互作用关系,以提升模型表示用户和课程的准确性. 爬取慕课网(MOOC)上的学习数据进行实验,结果表明,随着向量长度和推荐课程数的增加,IUNeu模型的性能增长速度较NeuMF模型更快;不同的消极采样量对2个模型的影响较大,模型性能随着消极采样量的增加而增加,当采样量达到一定值时,变化趋于稳定;IUNeu模型比NeuMF模型具有更高的收敛速度. 在IUNeu模型中加入更多课程特征信息,可以进一步提高IUNeu模型的推荐质量.

关键词: 课程推荐深度学习矩阵分解协同过滤神经网络    
Abstract:

Deep learning was introduced into course recommendation and a neural network model based on assistant information, item and user information (IUNeu) was proposed, aiming at the problem of sparse data and poor recommendation effect at online course recommendation. Based on the existing neural matrix factorization (NeuMF) model with integrated user and course information, the interaction between them was considered to improve the model accuracy of users and courses. Experiments were conducted by crawling the learning data on massive open online course (MOOC) online learning platform. Results showed that with the increase of vector length and the number of recommended courses, the performance of the IUNeu model increased faster than that of the NeuMF model. Different sampling quantities had a great impact on both two models. The models’ performance increased with the increasing sampling quantities, and when the sampling quantity reached a specific threshold, the performance became stable. The convergence rate of the IUNeu model was higher than that of the NeuMF model. The experimental results show that the recommendation quality can be further improved by adding more course feature information to the IUNeu model.

Key words: course recommendation    deep learning    matrix factorization    collaborative filtering    neural network
收稿日期: 2018-10-08 出版日期: 2019-11-21
CLC:  TP 391  
基金资助: 国家社会科学基金资助项目(17BTQ069);浙江省自然科学基金资助项目(LY19F020007)
通讯作者: 柳虹     E-mail: lixj@zjgsu.edu.cn;LLH@zjgsu.edu.cn
作者简介: 厉小军(1974—),男,教授,从事企业信息管理、自然语言处理研究. orcid.org/0000-0001-8480-5209. E-mail: lixj@zjgsu.edu.cn
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引用本文:

厉小军,柳虹,施寒潇,朱柳青,张亚辉. 基于深度学习的课程推荐模型[J]. 浙江大学学报(工学版), 2019, 53(11): 2139-2145.

Xiao-jun LI,Hong LIU,Han-xiao SHI,Liu-qing ZHU,Ya-hui ZHANG. Deep learning based course recommendation model. Journal of ZheJiang University (Engineering Science), 2019, 53(11): 2139-2145.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.11.011        http://www.zjujournals.com/eng/CN/Y2019/V53/I11/2139

图 1  NeuMF模型结构
图 2  IUNeu模型结构
图 3  潜在向量长度对模型性能的影响
图 4  推荐课程数对模型性能的影响
图 5  消极样本数量对模型性能的影响
图 6  不同迭代次数对模型性能的影响
图 7  课程特征对模型性能的影响
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