Analysing the data generated by the daily operations of enterprises through data mining technology can effectively predict customer loyalty and help enterprise leaders make correct decisions. Therefore, this paper classifies and analyses churn and loyalty of e-commerce customers, and by combining the application foundation of data mining technology in e-commerce customer loyalty prediction, a prediction model of e-commerce customer loyalty based on data mining is constructed. In this model, the local abnormal factor algorithm is used to eliminate the data for cleaning, the XGBoost algorithm is improved by adding penalty coefficient, and the prediction effect of the model is evaluated and compared according to the values of Accuracy, Precision, Recall and F. The results show that the model has high accuracy in predicting customer loyalty, which can accurately extract attributes of users and characteristic information of commodities.
There are many brands in cross-border e-commerce platforms. Obtaining consumers’ preference for brands will help promote the development of cross-border e-commerce industry. A brand preference prediction method of cross-border e-commerce consumers based on potential tag mining is proposed. Preprocess the cross-border e-commerce brand comment information obtained, build a HowNet emotion dictionary, and calculate consumers’ emotional tendency towards the brand on this basis. The projection pursuit regression model is optimized by differential evolution algorithm to reduce the dimension of the obtained consumer brand emotion information. Mining the potential labels of the information after dimensionality reduction, combined with Bayesian personalized sorting method and paired interaction tensor decomposition method, this paper constructs a brand preference’s prediction model to predict the brand preference of cross-border e-commerce consumers. The experimental results show that the proposed method has high accuracy of brand tendency calculation results, small average absolute error of prediction results, and high model accuracy.
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