Skip to main content
SpringerLink
Log in
Book cover

International Conference on Smart City and Informatization

iSCI 2019: Smart City and Informatization pp 180–196Cite as

Dynamic Online Course Recommendation Based on Course Network and User Network

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1122))

Abstract

E-learning attracts much attentions and gains sustainable development in recent years. Course recommendation tries to recommend proper courses to users from a large number of online courses. Existing works usually focus on improving the accuracy, neglecting to match the recommended course with user’s knowledge level. It results in a high enrollment rate but low grades, indicating poor learning results. Moreover, course recommendation also faces the challenges of sparse user-rating matrix and sparse social learning network. In this paper, we try to recommend courses that are fit to user’s knowledge level. To this end, we (1) propose to construct social learning network, for which we first build the user network and the course network, and combine them together; (2) explore the social learning network to extend the user-rating matrix by HITS algorithm, so as to overcome the sparsity challenge; (3) sort the recommendation list to meet user’s knowledge level, exploiting the course network. Experiments in a real e-learning dataset show that our model performs well in online course recommendation, and the learning results are better, validating the effectiveness of considering user’s knowledge level.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. https://open.163.com

  2. https://www.icourse163.org

  3. http://www.xuetangx.com

  4. Aher, S.B., Lobo, L.: Combination of machine learning algorithms for recommendation of courses in e-learning system based on historical data. Knowl.-Based Syst. 51, 1–14 (2013)

    Article  Google Scholar 

  5. Apaza, R.G., Cervantes, E.V., Quispe, L.C., Luna, J.O.: Online courses recommendation based on LDA. In: Proceedings of the 1st Symposium on Information Management and Big Data - SIMBig 2014, 8–10 October 2014, Cusco, Peru, pp. 42–48 (2014)

    Google Scholar 

  6. Bendakir, N., Aïmeur, E.: Using association rules for course recommendation. In: Proceedings of the AAAI Workshop on Educational Data Mining, vol. 3, pp. 1–10 (2006)

    Google Scholar 

  7. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. In: Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, 3–8 December 2001, Vancouver, British Columbia, Canada], pp. 601–608 (2001)

    Google Scholar 

  8. Boratto, L., Fenu, G., Marras, M.: The effect of algorithmic bias on recommender systems for massive open online courses. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds.) ECIR 2019. LNCS, vol. 11437, pp. 457–472. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15712-8_30

    Chapter  Google Scholar 

  9. Bridges, C., Jared, J., Weissmann, J., Montanez-Garay, A., Spencer, J.C., Brinton, C.G.: Course recommendation as graphical analysis. In: 52nd Annual Conference on Information Sciences and Systems, CISS 2018, 21–23 March 2018, Princeton, NJ, USA, pp. 1–6 (2018)

    Google Scholar 

  10. Chang, P.C., Lin, C.H., Chen, M.H.: A hybrid course recommendation system by integrating collaborative filtering and artificial immune systems. Algorithms 9(3), 47 (2016)

    Article  MathSciNet  Google Scholar 

  11. Gulzar, Z., Leema, A.A.: Course recommendation based on query classification approach. IJWLTT 13(3), 69–83 (2018)

    Google Scholar 

  12. Huang, L., Wang, C., Chao, H., Lai, J., Yu, P.S.: A score prediction approach for optional course recommendation via cross-user-domain collaborative filtering. IEEE Access 7, 19550–19563 (2019)

    Article  Google Scholar 

  13. Jiang, F., Tang, M., Tran, Q.A.: User preference-based spamming detection with coupled behavioral analysis. In: Wang, G., Ray, I., Alcaraz Calero, J.M., Thampi, S.M. (eds.) SpaCCS 2016. LNCS, vol. 10066, pp. 466–477. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49148-6_38

    Chapter  Google Scholar 

  14. Jiang, W., Pardos, Z.A., Wei, Q.: Goal-based course recommendation. CoRR abs/1812.10078 (2018)

    Google Scholar 

  15. Jiang, W., Jie, W., Feng, L., Wang, G., Zheng, H.: Trust evaluation in online social networks using generalized network flow. IEEE Trans. Comput. 65(3), 952–963 (2016)

    Article  MathSciNet  Google Scholar 

  16. Jiang, W., Jie, W., Wang, G.: On selecting recommenders for trust evaluation in online social networks. ACM Trans. Internet Technol. 15(4), 1–21 (2015)

    Article  Google Scholar 

  17. Jiang, W., Wang, G.: Swtrust: generating trusted graph for trust evaluation in online social networks. In: IEEE International Conference on Trust (2012)

    Google Scholar 

  18. Jing, X., Tang, J.: Guess you like: course recommendation in MOOCs. In: Proceedings of the International Conference on Web Intelligence, pp. 783–789. ACM (2017)

    Google Scholar 

  19. Kizilcec, R.F., Schneider, E.: Motivation as a lens to understand online learners: toward data-driven design with the OLEI scale. ACM Trans. Comput.-Hum. Interact. 22(2), 6:1–6:24 (2015)

    Article  Google Scholar 

  20. Kleinberg, J.M.: Hubs, authorities, and communities. ACM Comput. Surv. (CSUR) 31(4es), 5 (1999)

    Article  Google Scholar 

  21. Lee, E.L., Kuo, T.T., Lin, S.D.: A collaborative filtering-based two stage model with item dependency for course recommendation. In: 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 496–503. IEEE (2017)

    Google Scholar 

  22. Li, M., Jiang, W., Li, K.: Recommendation systems in real applications: algorithm and parallel architecture. In: Wang, G., Ray, I., Alcaraz Calero, J.M., Thampi, S.M. (eds.) SpaCCS 2016. LNCS, vol. 10066, pp. 45–58. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49148-6_5

    Chapter  Google Scholar 

  23. Li, X., Wang, T., Wang, H., Tang, J.: Understanding user interests acquisition in personalized online course recommendation. In: U, L.H., Xie, H. (eds.) APWeb-WAIM 2018. LNCS, vol. 11268, pp. 230–242. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01298-4_20

    Chapter  Google Scholar 

  24. Lin, J., Pu, H., Li, Y., Lian, J.: Sparse linear method based top-n course recommendation system with expert knowledge and \({L}_0\) regularization. In: Zu, Q., Hu, B. (eds.) HCC 2017. LNCS, vol. 10745, pp. 130–138. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74521-3_15

    Chapter  Google Scholar 

  25. Pappano, L.: The year of the MOOC. New York Times 2(12), 2012 (2012). http://www.nytimes.com/2012/11/04/education/edlife/massive-open-online-courses-are-multiplying-at-a-rapid-pace.html

  26. Parameswaran, A., Venetis, P., Garcia-Molina, H.: Recommendation systems with complex constraints: a course recommendation perspective. ACM Trans. Inf. Syst. (TOIS) 29(4), 20 (2011)

    Article  Google Scholar 

  27. Parameswaran, A.G., Garcia-Molina, H., Ullman, J.D.: Evaluating, combining and generalizing recommendations with prerequisites. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 919–928. ACM (2010)

    Google Scholar 

  28. Pardos, Z.A., Fan, Z., Jiang, W.: Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance. User Modeling User-Adapted Interact. 29(2), 487–525 (2019)

    Article  Google Scholar 

  29. Qiu, J., et al.: Modeling and predicting learning behavior in MOOCs. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, 22–25 February 2016, San Francisco, CA, USA, pp. 93–102 (2016)

    Google Scholar 

  30. Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_9

    Chapter  Google Scholar 

  31. Wang, G., Jiang, W., Wu, J., Xiong, Z.: Fine-grained feature-based social influence evaluation in online social networks. IEEE Trans. Parallel Distrib. Syst. 25(9), 2286–2296 (2014)

    Article  Google Scholar 

  32. Wang, Y., Liang, B., Ji, W., Wang, S., Chen, Y.: A weighted multi-attribute method for personalized recommendation in MOOCs. In: Proceedings of the 2nd International Conference on Crowd Science and Engineering, pp. 44–49. ACM (2017)

    Google Scholar 

  33. Zhang, H., Huang, T., Lv, Z., Liu, S., Zhou, Z.: MCRS: a course recommendation system for MOOCs. Multimedia Tools Appl. 77(6), 7051–7069 (2018)

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by NSFC grant 61632009 and Outstanding Young Talents Training Program in Hunan University 531118040173.

Author information

Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Xixi Yang & Wenjun Jiang

Authors
  1. Xixi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenjun Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenjun Jiang .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. University of Ottawa, Ottawa, ON, Canada

    Abdulmotaleb El Saddik

  3. Shanghai Jiao Tong University, Shanghai, China

    Xuejia Lai

  4. University of Murcia, Murcia, Spain

    Gregorio Martinez Perez

  5. The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yang, X., Jiang, W. (2019). Dynamic Online Course Recommendation Based on Course Network and User Network. In: Wang, G., El Saddik, A., Lai, X., Martinez Perez, G., Choo, KK. (eds) Smart City and Informatization. iSCI 2019. Communications in Computer and Information Science, vol 1122. Springer, Singapore. https://doi.org/10.1007/978-981-15-1301-5_15

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-1301-5_15

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1300-8

  • Online ISBN: 978-981-15-1301-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation