Design Method for Travel E-commerce Platform Based on HHO improved K-means Clustering Algorithm

被引:0
|
作者
Dang, Mihua [1 ]
Yang, Suiming [1 ]
机构
[1] Xian Traff Engn Inst, Sch Humanities & Management, Xian 710300, Shaanxi, Peoples R China
来源
EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS | 2024年 / 11卷 / 05期
关键词
travel E -commerce platform design; K -means clustering algorithm; Harris Hawk optimization algorithm; XGBoost;
D O I
10.4108/eetsis.5782
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Convenient and intelligent tourism product recommendation method, as the key technology of tourism E-commerce platform design, not only provides academic value to the research of tourism E-commerce platform, but also improves the efficiency of personalized recommendation of tourism products. In order to improve the quality of tourism recommendation, this paper proposes a tourism E-commerce platform design method based on HHO improved K-means clustering algorithm. Firstly, the Harris optimization algorithm is used to improve the K-means algorithm to construct a user-oriented tourism product recommendation strategy; then, combined with the XGBoost algorithm, an item-oriented tourism product recommendation strategy is proposed; secondly, the two strategies are mixed to construct a personalized tourism product recommendation model. Finally, the effectiveness of the proposed method is verified by simulation experiment analysis. The results show that the recommendation accuracy of the tourism E-commerce platform design method proposed in this paper reaches more than 90%, and the recommendation response time meets the real-time requirements, which can provide personalized tourism product recommendation for platform users and enhance the purchase of tourism products.
引用
收藏
页数:14
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