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

Mihua Dang; Suiming Yang

doi:10.4108/eetsis.5782

Authors

Mihua Dang Xi'an Traffic Engineering Institute
Suiming Yang Xi’an Traffic Engineering Institute

DOI:

https://doi.org/10.4108/eetsis.5782

Keywords:

travel E-commerce platform design, K-means clustering algorithm, Harris Hawk optimization algorithm, XGBoost

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.

References

Choy M W C , Kamoche K .Factors influencing recommendation of sub-Saharan Africa travel products: a Hong Kong-Kenya importance -performance analysis:[J].Tourism Economics, 2021.

Guixiang Z , Jie C .A Recommendation Engine for Travel Products Based on Topic Sequential Patterns[J]. 2018.

Lin T Y , Chan H T , Hsia C H , Lai C F. Facial Skincare Products' Recommendation with Computer Vision Technologies[J].Electronics, 2022, 11(1):143-.

Zeng D , Liu Y , Yan P , Yang Y. Location-Aware Real-Time Recommender Systems for Brick-and-Mortar Retailers[J].Informs Journal on Computing, 2021(7 ).

Esmaeili L , Mardani S , Golpayegani S A H , Madar Z Z. A novel tourism recommender system in the context of social commerce[J].Expert Systems with Application, 2020.

Chang J L , Li H , Bi J W .Personalized travel recommendation: a hybrid method with collaborative filtering and social network analysis[J]. issues in tourism, 2022.

Roy D , Dutta M .An Improved Cat Swarm Search-Based Deep Ensemble Learning Model for Group Recommender Systems[J]. Knowledge Management, 2022.

Xing X , Li X .Recommendation of urban vehicle driving routes under traffic congestion: a traffic congestion regulation method considering road network equilibrium[J].Computers and Electrical Engineering, 2023:110.

Wang X .Personalized recommendation framework design for online tourism: know you better than yourself[J].Industrial Management & Data Systems, 2020, 120(11):2067-2079.

Chen L , Cao J , Wang Y , Liang W, Zhu G. Multi-view Graph Attention Network for Travel Recommendation[J].Expert Systems with Applications, 2022, 191. 116234-.

Lee,Hae-Young.What can be expected from the attached travel members? - The moderating role of travel knowledge[J].Korean Journal of Tourism Research, 2011, 26(3):377-393.

Chen L , Wu Z , Cao J , Zhu G, Ge Y. Travel Recommendation via Fusing Multi-Auxiliary Information into Matrix Factorization[J].ACM Transactions on Intelligent Systems and Technology (TIST), 2020.

Nitu P , Coelho J , Madiraju P .Improvising Personalized Travel Recommendation System with Recency Effects[J]. Big Data Mining and Analytics(English), 2021(3):16.

Chen L , Cao J , Liang J Y Q .Keywords-enhanced Deep Reinforcement Learning Model for Travel Recommendation[J].ACM transactions on the web, 2023, 17( 1):1.1-1.21.

Zhu Z , Wang S , Wang F , Tu Z. Recommendation networks of homogeneous products on an E-commerce platform: measurement and competition effects[J]. Expert Systems with Application, 2022(09):201.

Manwade P P N K B .Survey on Recommendation of E-commerce products to existing and new user by Analyzing Social data along with E-commerce data[J]. International Journal for Modern Trends in Science and Technology, 2020, 6(7):78-84.

Tang Q , Zhong S .A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots[J]. robotics, 2022(Pt.1):2022.

Chang L V .Recommendation and Sentiment Analysis Based on Consumer Review and Rating[J]. , 2020, 11(2).

Wan H , Li Y .A personalized recommendation algorithm of user preference products based on Bayesian network[J]. Development, 2021, 25(2):85-.

Yoon S , Lee T H .The Effect of the Perceived Lovemark Constructs of Travel Agency on Repurchase/Recommendation Intention and Consumer Support toward the Extension of Discounted Travel Products[J].Journal of Korea Service Management Society, 2013, 14(1):49-72.

Cui C , Wei M , Che L , Wu S, Wang E. Hotel recommendation algorithms based on online reviews and probabilistic linguistic term sets[J].Expert Systems with Application, 2022.

Xu B , Chen Z , Wang X , Bu J, Zhu Z, Zhang H. Combined prediction model of concrete arch dam displacement based on cluster analysis considering signal residual correction[J].Mechanical Systems & Signal Processing, 2023:203.

Chen G , Cockburn B , Singler J R , Zhang Y. Superconvergent Interpolatory HDG Methods for Reaction Difusion Equations II: HHO-Inspired Methods[J]. Journal of Applied and Computational Mathematics (in English), 2022, 4(2):23.

Ji W , Liao Y , Zhang L .Integrated Generative Adversarial Network and XGBoost for Anomaly Processing of Massive Data Flow in Dispatch Automation Systems[J]. Intelligent Automation and Soft Computing(English), 2023, 37(9):2825-2848.

Shen Weilei, Zhang Xinyang, Yang Xuechun. Research on quality control method of clutch manufacturing process based on random forest[J]. Journal of Hefei University of Technology (Natural Science Edition), 2023(11):1441-1446,1500.