Brand Preference Prediction Method of Cross-Border E-Commerce Consumers Based on Potential Tag Mining

Abstract

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.