Open Access
ARTICLE
Design of Hybrid Recommendation Algorithm in Online Shopping System
Yingchao Wang1, Yuanhao Zhu1, Zongtian Zhang1, Huihuang Liu1,* , Peng Guo2
1 Hunan University of Finance and Economics, Changsha, China
2 University Malaysia Sabah, Kota Kinabalu, Malaysia
* Corresponding Author:Huihuang Liu. Email:
Journal of New Media 2021, 3(4), 119-128. https://doi.org/10.32604/jnm.2021.016655
Received 15 April 2021; Accepted 11 September 2021; Issue published 05 November 2021
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.
Keywords
Cite This Article
Y. Wang, Y. Zhu, Z. Zhang, H. Liu and ,. Peng Guo, "Design of hybrid recommendation algorithm in online shopping system,"
Journal of New Media, vol. 3, no.4, pp. 119–128, 2021. https://doi.org/10.32604/jnm.2021.016655