Open Access
ARTICLE
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:
Journal on Internet of Things 2021, 3(2), 65-76. https://doi.org/10.32604/jiot.2021.015401
Received 07 January 2021; Accepted 11 April 2021; Issue published 15 July 2021
Abstract
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.
Keywords
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. https://doi.org/10.32604/jiot.2021.015401