Computer Technology and Control Engineering |
|
|
|
|
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 |
|
|
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.
|
Received: 08 October 2018
Published: 21 November 2019
|
|
Corresponding Authors:
Hong LIU
E-mail: lixj@zjgsu.edu.cn;LLH@zjgsu.edu.cn
|
基于深度学习的课程推荐模型
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
|
|
[1] |
PAZZANIM J, BILLSUS D. Content-based recommendation systems [M]// The adaptive web. Berlin, Heidelberg: Springer, 2007: 325-341.
|
|
|
[2] |
CHEN T, HONG L J, SHI Y, et al. Joint text embedding for personalized content-based recommendation [EB/OL]. (2017-06-23). http://arxiv.org/abs/1706.01084.
|
|
|
[3] |
ALMALIS N D, TSIHRINTZIS G A, KARAGIANNIS N, et al. FoDRA: a new content-based job recommendation algorithm for job seeking and recruiting [C]// 2015 6th International Conference on Information, Intelligence, Systems and Applications. Corfu: IEEE, 2015: 1-7.
|
|
|
[4] |
ZHOU X, PARANDEHGHEIBI M, FEI X, et al. Content-based recommendation for podcast audio-items using natural language processing techniques [C]// 2016 IEEE International Conference on Big Data. Washington DC: IEEE, 2016: 2378-2383.
|
|
|
[5] |
OLVERA E P, GODOY D Evaluating term weighting schemes for content-based tag recommendation in social tagging systems[J]. IEEE Latin America Transactions, 2012, 10 (4): 1973- 1980
doi: 10.1109/TLA.2012.6272482
|
|
|
[6] |
WANG P F, GUO J F, LAN Y Y, et al. Learning hierarchical representation model for nextbasket recommendation [C]// Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2015: 403-412.
|
|
|
[7] |
SINGH A P, GORDON G J. Relational learning via collective matrix factorization [C]// Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas: ACM, 2008: 650-658.
|
|
|
[8] |
SEDHAIN S, MENON A K, SANNER S, et al. Autorec: autoencoders meet collaborative filtering [C]// Proceedings of the 24th International Conference on World Wide Web. Florence: ACM, 2015: 111-112.
|
|
|
[9] |
COVINGTON P, ADAMS J, SARGIN E. Deep neural networks for youtube recommendations [C]// Proceedings of the 10th ACM Conference on Recommender Systems. Boston: ACM, 2016: 191-198.
|
|
|
[10] |
SALAKHUTDINOV R, MNIH A. Bayesian probabilistic matrix factorization using markov chain monte carlo [C]// Proceedings of the 25th International Conference on Machine Learning. Helsinki: ACM, 2008: 880-887.
|
|
|
[11] |
MA H, YANG H X, LYU M R, et al. Sorec: social recommendation using probabilistic matrix factorization [C]// Proceedings of the 17th ACM Conference on Information and Knowledge Management. Napa Valley: ACM, 2008: 931-940.
|
|
|
[12] |
DEVOOGHT R, BERSINI H. Collaborative filtering with recurrent neural networks [EB/OL]. (2017-01-03). http://arxiv.org/abs/1608.07400.
|
|
|
[13] |
WANG C, BLEI D M. Collaborative topic modeling for recommending scientific articles [C]// Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego: ACM, 2011: 448-456.
|
|
|
[14] |
WANG H, WANG N Y, YEUNG D Y. Collaborative deep learning for recommender systems [C]// Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Sydney: ACM, 2015: 1235-1244.
|
|
|
[15] |
HE X N, LIAO L Z, ZHANG H W, et al. Neural collaborative filtering [C]// Proceedings of the 26th International Conference on World Wide Web. Perth: International World Wide Web Conferences Steering Committee, 2017: 173-182.
|
|
|
[16] |
CHEN W, NIU Z D, ZHAO X Y, et al A hybrid recommendation algorithm adapted in e-learning environments[J]. World Wide Web, 2014, 17 (2): 271- 284
doi: 10.1007/s11280-012-0187-z
|
|
|
[17] |
LECUN Y, BENGIO Y, HINTON G Deep learning[J]. Nature, 2015, 521 (7553): 436- 444
doi: 10.1038/nature14539
|
|
|
[18] |
ELKAHKY A, SONG Y, HE X D. A multi-view deep learning approach for cross domain user modeling in recommendation systems [C]// Proceedings of the 24th International Conference on World Wide Web. Florence: ACM, 2015: 278-288.
|
|
|
[19] |
ZHENG L, NOROOZI V, YU P S. Joint deep modeling of users and items using reviews for recommendation [C]// Proceeding of the 10th ACM International Conference on Web Search and Data Mining. Cambridge: ACM, 2017: 425-434.
|
|
|
[20] |
XU Z H, CHEN C, LUKASIEWICZ T, et al. Tag-aware personalized recommendation using a deep-semantic similarity model with negative sampling [C]// Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. Indianapolis: ACM, 2016: 1921-1924.
|
|
|
[21] |
CHEN C, MENG X W, XU Z H, et al Location-aware personalized news recommendation with deep semantic analysis[J]. IEEE Access, 2017, 5 (99): 1624- 1638
|
|
|
[22] |
OKURA S, TAGAMI Y, ONO S, et al. Embedding-based news recommendation for millions of users [C]// Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Halifax: ACM, 2017: 1933-1942.
|
|
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|