Abstract
Improving the recommendation accuracy is the ultimate goal of the recommendation system. Based on the recommendation accuracy, a variety of similarity calculation methods are compared and analyzed. Combined with the deep learning mode, a multi-dimensional similarity personalized recommendation model is built in the deep learning mode. Coefficient parameters, repeated training of training and adjustment coefficients in the deep learning training center, determine the personalized recommendation model scheme, through the simulation experiment, the proposed personalized recommendation model can effectively determine the user recommendation list and improve the accuracy of personalized recommendation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jiang, Wei. Research on several key technologies of the recommendation system.
Lian, Jianxun. 2018. Personalized recommendation system based on diversified content data.
Yu, Xin. Hybrid collaborative filtering recommendation algorithm based on deep learning and social relationship regularization.
Gao, Quanli, Gao Ling, Yang Jianfeng, et al. 2015. Preference acquisition method based on user cognitive behavior in context-aware recommendation system. Chinese Journal of Computers 38 (9).
Yunfei, Zheng, and Xia Shuai. 2014. Design and implementation of user-based collaborative filtering recommendation system for agricultural products. Agriculture Network Information 9: 49–53.
Deng, Feng, and Zhang Yong-an. 2015. Collaborative filtering recommendation system based on multi-attribute utility. Journal of Computer Applications 35 (7): 1988–1992.
Li, Guang. Research on recommendation algorithm based on cyclic neural network.
Feng, Liu, and Weiwei Guo. Research on algorithm of personalized recommendation system based on deep learning. In 9th International Conference on Management and Computer Science (ICMCS 2019), 162–168.
Suo, Qi, and Lu Tao. 2005. Research on e-commerce recommendation system based on association rules. Journal of Natural Science of Harbin Normal University 21 (2): 50–53.
Feng, L., and G. Wei-Wei. 2018. Recommendation algorithm based on tag time weighting. In 2018 International Conference on Smart City and Systems Engineering (ICSCSE), IEEE Computer Society, 756–759.
Acknowledgements
This research was supported by the project of Nature Scientific Foundation of Heilongjiang Province (F2016038).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, F., Guo, W. (2020). Multi-dimensional Similarity Personalized Recommendation Model in Deep Learning Mode. In: Huang, C., Chan, YW., Yen, N. (eds) Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Advances in Intelligent Systems and Computing, vol 1088. Springer, Singapore. https://doi.org/10.1007/978-981-15-1468-5_81
Download citation
DOI: https://doi.org/10.1007/978-981-15-1468-5_81
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1467-8
Online ISBN: 978-981-15-1468-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)