In recent years, blockchain has substantially enhanced the credibility of e-commerce platforms for users. The prediction accuracy of the repeat purchase behaviour of e-commerce users directly affects the impact of precision marketing by merchants. The existing ensemble learning models have low prediction accuracy when the purchase behaviour sample is unbalanced and the information dimension of feature engineering is single. To overcome this problem, an ensemble learning prediction model based on multisource information fusion is proposed. Tests on the Tmall dataset showed that the accuracy and AUC values of the model reached 91.28% and 70.53%, respectively.

中文翻译：

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基于区块链的电子商务平台回头客预测

近年来，区块链极大地增强了用户电子商务平台的信誉。电子商务用户重复购买行为的预测准确性直接影响商人进行精确营销的影响。当购买行为样本不平衡且特征工程的信息维度单一时，现有的集成学习模型的预测准确性较低。为了克服这个问题，提出了一种基于多源信息融合的集成学习预测模型。在天猫数据集上的测试表明，该模型的准确性和AUC值分别达到91.28％和70.53％。