@Article{jnm.2021.016655,
AUTHOR = {Yingchao Wang, Yuanhao Zhu, Zongtian Zhang, Huihuang Liu , Peng Guo},
TITLE = {Design of Hybrid Recommendation Algorithm in Online Shopping System},
JOURNAL = {Journal of New Media},
VOLUME = {3},
YEAR = {2021},
NUMBER = {4},
PAGES = {119--128},
URL = {http://www.techscience.com/JNM/v3n4/45474},
ISSN = {2579-0129},
ABSTRACT = {In order to improve user satisfaction and loyalty on e-commerce 
websites, recommendation algorithms are used to recommend products that may 
be of interest to users. Therefore, the accuracy of the recommendation algorithm 
is a primary issue. So far, there are three mainstream recommendation algorithms, 
content-based recommendation algorithms, collaborative filtering algorithms and 
hybrid recommendation algorithms. Content-based recommendation algorithms 
and collaborative filtering algorithms have their own shortcomings. The contentbased recommendation algorithm has the problem of the diversity of 
recommended items, while the collaborative filtering algorithm has the problem 
of data sparsity and scalability. On the basis of these two algorithms, the hybrid 
recommendation algorithm learns from each other’s strengths and combines the 
advantages of the two algorithms to provide people with better services. This 
article will focus on the use of a content-based recommendation algorithm to 
mine the user’s existing interests, and then combine the collaborative filtering 
algorithm to establish a potential interest model, mix the existing and potential 
interests, and calculate with the candidate search content set. The similarity gets 
the recommendation list.},
DOI = {10.32604/jnm.2021.016655}
}