With the rapid development of wireless communication network, M-Commerce has achieved great success. Users leave a lot of historical behavior data when shopping on the M-Commerce platform. Using these data to predict future purchasing behaviors of the users will be of great significance for improving user experience and realizing mutual benefit and win-win result between merchant and user. Therefore, a sample balance-based multi-perspective feature ensemble learning was proposed in this study as the solution to predicting user purchasing behaviors, so as to acquire user’s historical purchasing behavioral data with sample balance. Influence feature of user purchasing behaviors was extracted from three perspectives—user, commodity and interaction, in order to further enrich the feature dimensions. Meanwhile, feature selection was carried out using XGBSFS algorithm. Large-scale real datasets were experimented on Alibaba M-Commerce platform. The experimental results show that the proposed method has achieved better prediction effect in various evaluation indexes such as precision and recall rate.

随着无线通信网络的飞速发展，M-Commerce取得了巨大的成功。在M-Commerce平台上购物时，用户会留下很多历史行为数据。利用这些数据来预测用户未来的购买行为，对于改善用户体验，实现商家与用户之间的互利双赢具有重要意义。因此，本研究提出了一种基于样本余额的多视角特征集成学习方法，作为预测用户购买行为的解决方案，从而利用样本余额获取用户的历史购买行为数据。从用户，商品和交互三个角度提取了用户购买行为的影响特征，以进一步丰富特征维度。与此同时，使用XGBSFS算法进行特征选择。在阿里巴巴M-Commerce平台上对大规模真实数据集进行了实验。实验结果表明，该方法在精度，召回率等各种评价指标上均取得了较好的预测效果。