Abstract
With the explosion of data on the online course website, getting the required course information quickly and accurately becomes more and more difficult. In this paper, probability matrix factorization algorithm for course recommendation system fusing the influence of nearest neighbor users based on cloud model is proposed. The proposed algorithm uses the cloud model to compute user similarity and integrates social information into the course recommendation. The experimental results show that the algorithm can improve the accuracy of course recommendation effectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xia, Z., Song, A., Fang, D., et al.: A collaborative filtering recommendation mechanism for cloud computing. J. Comput. Res. Dev. 51(10), 2255–2269 (2014)
Chen, L., Chen, G., Wang, F.: Recommender systems based on user reviews: the state of the art. User Mod. User-Adap. Inter. 25(2), 99–154 (2015)
Goldberg, D., Nichols, D., Oki, B.M., et al.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)
Resnick, P., Iacovou, N., Suchak, M., et al.: GroupLens: an open architecture for collaborative filtering of netnews. In: ACM Conference on Computer Supported Cooperative Work 1994, pp. 175–186. ACM, Chapel Hill (1994)
Pagare, R.A., Patil, S.: Study of collaborative filtering recommendation algorithm scalability issue. Int. J. Comput. Appl. 67(25), 10–15 (2014)
Pham, T.A.N., Li, X., Cong, G., et al.: A general graph-based model for recommendation in event-based social networks. In: International Conference on Data Engineering 2015, pp. 567–578. IEEE (2015)
Bokde, D., Girase, S., Mukhopadhyay, D.: Matrix factorization model in collaborative filtering algorithms: a survey. Procedia Comput. Sci. 49, 136–146 (2015). Icac
Song, Y., Zhuang, Z., Zhao, Q., et al.: Real-time automatic tag recommendation. In: International ACM SIGIR Conference on Research and Development in Information Retrieval 2008, vol. 6, pp. 515–522. ACM (2008)
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. Adv. Neural. Inf. Process. Syst. 20(2), 1257–1264 (2007)
Li J, Xia F, Wang W, et al.: ACRec: a co-authorship based random walk model for academic collaboration recommendation. International Conference on World Wide Web 2014, pp. 1209–1214. ACM (2014)
Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: ACM International Conference on Web Search and Data Mining 2010, pp. 81–90. ACM (2010)
Zhang, G.-W., Li, D.-Y., Li, P., et al.: A collaborative filtering recommendation algorithm based on cloud model. J. Softw. 18(10), 2403–2411 (2007)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (No. 61272067), the Science and Technology Project of Guangdong Province (Nos. 2017A040405057 and 2016A030303058), and the Science and Technology Program of Guangzhou, China (No. 201604046017).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, J., Chang, C., Yang, Z., Fu, H., Tang, Y. (2019). Probability Matrix Factorization Algorithm for Course Recommendation System Fusing the Influence of Nearest Neighbor Users Based on Cloud Model. In: Tang, Y., Zu, Q., RodrÃguez GarcÃa, J. (eds) Human Centered Computing. HCC 2018. Lecture Notes in Computer Science(), vol 11354. Springer, Cham. https://doi.org/10.1007/978-3-030-15127-0_49
Download citation
DOI: https://doi.org/10.1007/978-3-030-15127-0_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15126-3
Online ISBN: 978-3-030-15127-0
eBook Packages: Computer ScienceComputer Science (R0)