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  • Open Access


    Design of Hybrid Recommendation Algorithm in Online Shopping System

    Yingchao Wang1, Yuanhao Zhu1, Zongtian Zhang1, Huihuang Liu1,* , Peng Guo2

    Journal of New Media, Vol.3, No.4, pp. 119-128, 2021, DOI:10.32604/jnm.2021.016655

    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… More >

  • Open Access


    A Novel Collaborative Filtering Algorithm and Its Application for Recommendations in E-Commerce

    Jie Zhang1,5, Juan Yang2,*, Li Wang3, Yizhang Jiang4, Pengjiang Qian4, Yuan Liu4

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1275-1291, 2021, DOI:10.32604/cmes.2021.012112

    Abstract With the rapid development of the Internet, the amount of data recorded on the Internet has increased dramatically. It is becoming more and more urgent to effectively obtain the specific information we need from the vast ocean of data. In this study, we propose a novel collaborative filtering algorithm for generating recommendations in e-commerce. This study has two main innovations. First, we propose a mechanism that embeds temporal behavior information to find a neighbor set in which each neighbor has a very significant impact on the current user or item. Second, we propose a novel collaborative filtering algorithm by injecting… More >

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