Abstract
With the rapid development of Internet technology, the number of groups participating in online learning is increasing. Online learning, interaction and communication are becoming more frequent. Online education has gradually formed as a new type of education model. On the one hand, it brings great convenience to students and provides a new way of learning. On the other hand, it also proposes solutions to the phenomenon of "information overload" brought about by the rapid growth of learning resources. A distinctive feature of online education is interest-driven. The traditional online education course recommendation method has poor topic concentration due to the problem of data sparse. For this reason, an online education course recommendation method based on the LDA (Latent Dirichlet Allocation) user interest model is designed, where LDA is an unsupervised machine learning method. The LDA user interest model is used to judge the user's preference for topics, obtain the user's interest in online education courses, and complete the recommendation of online education courses based on this. Finally, the proposed method is evaluated using online learning website data. The comparison results with three online course recommendation methods show that the recommendation method based on the LDA model has better recommendation effect, and the recommended course topics are more concentrated, which is more suitable for application in online education course recommendation.
Similar content being viewed by others
Data availability
All data used to support the findings of the study is included within this paper.
References
Douglas KA, Bermel P, Alam MM, Madhavan K (2016) Big data characterization of learner behaviour in a highly technical MOOC engineering course. J Learn Anal 3(3):170–192
Khanal SS, Prasad PWC, Alsadoon A, Maag A (2020) A systematic review: machine learning based recommendation systems for e-learning. Educ Inf Technol 25(4):2635–2664
Wu P, Yu S, Wang D (2018) Using a learner-topic model for mining learner interests in open learning environments. J Educ Technol Soc 21(2):192–204
Liu S, Ni C, Liu Z, Peng X, Cheng HN (2017) Mining individual learning topics in course reviews based on author topic model. Int J Dist Educ Technol (IJDET) 15(3):1–14
Dun Y, Wang N, Wang M, Hao T (2017) Revealing learner interests through topic mining from question-answering data. Int J Dist Educ Technol (IJDET) 15(2):18–32
Xu B, Yang D (2015) Study partners recommendation for xMOOCs learners. Comput Intell Neurosci
Thanh-Nhan HL, Nguyen HH, Thai-Nghe N (2016) Methods for building course recommendation systems. In: 2016 Eighth international conference on knowledge and systems engineering (KSE), pp. 163–168. IEEE
Obeidat R, Duwairi R, Al-Aiad A (2019) A collaborative recommendation system for online courses recommendations. In: 2019 International conference on deep learning and machine learning in emerging applications (Deep-ML), pp. 49–54. IEEE.
Huang Z, Liu Q, Zhai C, Yin Y, Chen E, Gao W, Hu G (2019) Exploring multi-objective exercise recommendations in online education systems. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp. 1261–1270
Liu Q, Tong S, Liu C, Zhao H, Chen E, Ma H, Wang S (2019) Exploiting cognitive structure for adaptive learning. In: Proceedings of the 25th ACM SIGKDD International conference on knowledge discovery and data mining, pp. 627–635
Zhang H, Huang T, Lv Z, Liu S, Zhou Z (2018) MCRS: a course recommendation system for MOOCs. Multimedia Tools Appl 77(6):7051–7069
Chen Y, Li X, Liu J, Ying Z (2018) Recommendation system for adaptive learning. Appl Psychol Meas 42(1):24–41
Tang C (2021) Recommendation system for adaptive learning. Hong Kong University of Science and Technology (Hong Kong)
Lei C, Maoting G (2019) Hybrid recommendation algorithm based on time weighted and LDA clustering. Comput Eng Appl 55(11):160–166
Yang F, Xie H, Li H (2019). RETRACTED: video associated cross-modal recommendation algorithm based on deep learning
Sharma S, Rana V, Malhotra M (2022) Automatic recommendation system based on hybrid filtering algorithm. Educ Inf Technol 27(2):1523–1538
Basilico J, Raimond Y (2017) Déja vu: The importance of time and causality in recommender systems. In: Proceedings of the eleventh ACM conference on recommender systems, pp. 342–342
Jin X, Zheng Q, Sun L (2015) An optimization of collaborative filtering personalized recommendation algorithm based on time context information. International conference on informatics and semiotics in organisations. Springer, Cham, pp 146–155
Hu Y, Peng Q, Hu X, Yang R (2015) Web service recommendation based on time series forecasting and collaborative filtering. In: 2015 IEEE international conference on web services, pp. 233–240
Zheng W, Ge B, Wang C (2019) Building a TIN-LDA model for mining microblog users’ interest. IEEE Access 7:21795–21806
Xiao Z, Che F, Miao E, Lu M (2014) Increasing serendipity of recommender system with ranking topic model. Appl Math Inf Sci 8(4):2041
Shao X, Tang G, Bao BK (2019) Personalized travel recommendation based on sentiment-aware multimodal topic model. IEEE Access 7:113043–113052
Liu W, Pang J, Li N, Zhou X, Yue F (2021) Research on multi-label text classification method based on tALBERT-CNN. Int J Comput Intell Syst 14(1):1–12
Zhang H, Almeroth K (2010) Moodog: tracking student activity in online course management systems. J Interact Learn Res 21(3):407–429
Li X, Wang T, Wang H, Tang J (2018) Understanding user interests acquisition in personalized online course recommendation. Asia-pacific web (APWeb) and web-age information management (WAIM) joint international conference on web and big data. Springer, Cham, pp 230–242
Xing S, Fan Z (2020) A Method for LDA-based Sina Weibo Recommendation. In: Proceedings of the 2020 2nd international conference on big data engineering and technology, pp. 54–57
Bagul DV, Barve S (2021) A novel content-based recommendation approach based on LDA topic modeling for literature recommendation. In: 2021 6th International conference on inventive computation technologies (ICICT), pp. 954–961
Funding
This paper has not received any funding support yet.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Jiang, X., Bai, L., Yan, X. et al. LDA-based online intelligent courses recommendation system. Evol. Intel. 16, 1619–1625 (2023). https://doi.org/10.1007/s12065-022-00810-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12065-022-00810-2