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Research Article

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

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  • @ARTICLE{10.4108/eetsis.5782,
        author={Mihua Dang and Suiming Yang},
        title={Design Method for Travel E-commerce Platform Based on HHO imparoved K-means Clustering Algorithm},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={5},
        publisher={EAI},
        journal_a={SIS},
        year={2024},
        month={4},
        keywords={travel E-commerce platform design, K-means clustering algorithm, Harris Hawk optimization algorithm, XGBoost},
        doi={10.4108/eetsis.5782}
    }
    
  • Mihua Dang
    Suiming Yang
    Year: 2024
    Design Method for Travel E-commerce Platform Based on HHO imparoved K-means Clustering Algorithm
    SIS
    EAI
    DOI: 10.4108/eetsis.5782
Mihua Dang1,*, Suiming Yang2
  • 1: Xi'an Traffic Engineering Institute
  • 2: Xi’an Traffic Engineering Institute
*Contact email: 443721376@qq.com

Abstract

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.

Keywords
travel E-commerce platform design, K-means clustering algorithm, Harris Hawk optimization algorithm, XGBoost
Received
2023-12-29
Accepted
2024-04-22
Published
2024-04-26
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.5782

Copyright © 2024 Dang et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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