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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.