Application of Sample Balance-Based Multi-Perspective Feature Ensemble Learning for Prediction of User Purchasing Behaviors on Mobile Wireless Network Platforms
In recent years, with the rapid development of wireless communication network, M-Commerce has achieved great success. Relying on mobile phones, tablets and other wireless communication devices for online shopping has become a mainstream way for users to consume. 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
... 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, specifically including: 1) "Sliding window"-centroid under-sampling was combined with sample balance method was used, while the positive sample size was enlarged using "sliding window", centroid under-sampling was used to reduce the negative sample size within "sliding window", so as to acquire user's historical purchasing behavioral data with sample balance. 2) Influence feature of user purchasing behaviors were 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. 3) An ensemble learning model—five-fold cross validation stacking—which could be used to predict user purchasing behaviors was raised. Three prediction models—XGBoost-Logistics, LightGBM-L2 and cascaded deep forest models—so that they could realize mutual collaboration and the overall prediction ability of the ensemble learning model could be improved. 4) 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.