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Fusion of Internal Similarity to Improve the Accuracy of Recommendation Algorithm

Zejun Yang1, Denghui Xia1, Jin Liu1, Chao Zheng2, Yanzhen Qu1,3,4, Yadang Chen1, Chengjun Zhang1,2,3,*

1 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 Yukun (Beijing) Network Technology Co., Ltd., Beijing, 102200, China
3 Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing, 210044, China
4 School of Computer Science and Technology, Colorado Technical University, Colorado Springs, 80907, USA

* Corresponding Author: Chengjun Zhang. Email: email

Journal on Internet of Things 2021, 3(2), 65-76.


Collaborative filtering algorithms (CF) and mass diffusion (MD) algorithms have been successfully applied to recommender systems for years and can solve the problem of information overload. However, both algorithms suffer from data sparsity, and both tend to recommend popular products, which have poor diversity and are not suitable for real life. In this paper, we propose a user internal similarity-based recommendation algorithm (UISRC). UISRC first calculates the item-item similarity matrix and calculates the average similarity between items purchased by each user as the user’s internal similarity. The internal similarity of users is combined to modify the recommendation score to make score predictions and suggestions. Simulation experiments on RYM and Last.FM datasets, the results show that UISRC can obtain better recommendation accuracy and a variety of recommendations than traditional CF and MD algorithms.


Cite This Article

Z. Yang, D. Xia, J. Liu, C. Zheng, Y. Qu et al., "Fusion of internal similarity to improve the accuracy of recommendation algorithm," Journal on Internet of Things, vol. 3, no.2, pp. 65–76, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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