Zejun Yang1, Denghui Xia1, Jin Liu1, Chao Zheng2, Yanzhen Qu1,3,4, Yadang Chen1, Chengjun Zhang1,2,3,*
Journal on Internet of Things, Vol.3, No.2, pp. 65-76, 2021, DOI:10.32604/jiot.2021.015401
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… More >
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