Abstract
Traditional recommendation algorithms are faced with the problems of low recommendation accuracy and cold start. To solve these problems, a personalized recommendation algorithm model based on user profile and user preference is proposed. It is more effective than the traditional collaborative filtering recommendation algorithm based on users. Firstly, the user model is established based on the user’s historical rating data. When calculating the user similarity, the user profile information is integrated into the user model. Finally, according to the individual profile differences between different users, the user profile parameters are adjusted to further optimize the user model. The experimental results show that the personalized recommendation algorithm based on user profile and user preference model can effectively improve the performance of the recommendation system.
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
Bell, R.M., Koren, Y.: Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9, 75–79 (2017)
Bell, R.M., Koren, Y., Volinsky, C.: Modeling relationships at multiple scales to improve accuracy of large recommender systems. In: Proceedings of the International Conference on Knowledge Discovery & Data Mining (2007)
Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. 3, 1–22 (2012)
Hu, L., Song, G.H., Xie, Z.Z., Zhao, K.: Personalized recommendation algorithm based on preference features. Tsinghua Sci. Technol. 19, 293–299 (2014)
Li, W.J., Dong, Q., Fu, Y.: Investigating the temporal effect of user preferences with application in movie recommendation. Mob. Inf. Syst. 10 (2017)
Lee, J., Lee, D., Lee, Y.C., Hwang, W.S., Kim, S.W.: Improving the accuracy of top-N recommendation using a preference model. Inf. Sci. 348, 290–304 (2016)
Wang, J.Q., Wang, Z.J., Li, J.: A recommendation method for social collaboration tasks based on personal social preferences. IEEE Access 6, 11 (2018)
Inuzuka, K., Hayashi, T., Takagi, T.: Recommendation system based on prediction of user preference changes. In: IEEE/WIC/ACM International Conference on Web Intelligence. IEEE (2017)
Zhu, Z.L., Li, D.Y., Liang, J., Liu, G.Q., Yu, H.: A dynamic personalized news recommendation system based on BAP user profiling method. IEEE Access 6, 41068–41078 (2018)
Sahu, A.K., Dwivedi, P.: User profile as a bridge in cross-domain recommender systems for sparsity reduction. Appl. Intell. 49, 2461–2481 (2019)
Hu, X., Liu, Q., Li, L.: A high precision recommendation algorithm based on combination features. In: International Conference on Database Systems for Advanced Applications, pp. 74–86. Springer, Cham (2018)
Acknowledgments
This research is supported by National Key Research and Development Program of China under grant number 2017YFC1405403, and the Philosophical and Social Sciences Research Project of Hubei Education Department under Grant 19Q054, and Green Industry Technology Leding Project (product development category) of Hubei University of Technology under grant number CPYF2017008.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhou, L., Xiong, C., Deng, N., Shen, L. (2020). Personalized Recommendation Algorithm Based on User Preference and User Profile. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing, vol 1150. Springer, Cham. https://doi.org/10.1007/978-3-030-44038-1_110
Download citation
DOI: https://doi.org/10.1007/978-3-030-44038-1_110
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44037-4
Online ISBN: 978-3-030-44038-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)