@Article{jiot.2021.015401,
AUTHOR = {Zejun Yang, Denghui Xia, Jin Liu, Chao Zheng, Yanzhen Qu, Yadang Chen, Chengjun Zhang},
TITLE = {Fusion of Internal Similarity to Improve the Accuracy of Recommendation Algorithm},
JOURNAL = {Journal on Internet of Things},
VOLUME = {3},
YEAR = {2021},
NUMBER = {2},
PAGES = {65--76},
URL = {http://www.techscience.com/jiot/v3n2/43102},
ISSN = {2579-0080},
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.},
DOI = {10.32604/jiot.2021.015401}
}