A Model Fusion Approach for Goods Information Inspection in Dual-Platform E-Commerce Systems

In nowadays, the large-scale e-commerce corporations tend to operate their own logistics networks to guarantee the speed, safety and economic efficiency of goods delivery. Thus, an e-commerce corporation usually needs to manage an online retailing platform and a logistics platform simultaneously. Despite the offered convenience, the cross-platform management faces grand challenges on security. The inappropriate philosophies for maintaining goods information and malicious behaviors of some merchants may induce mismatch between the goods and corresponding information, which consequently leads to incorrect delivery and the degradation of customer experience. In order to tackle this issue, an innovative model fusion method is proposed in this work for goods information inspection. It investigates the advantages of multiple natural language processing models as well as domain knowledge to extract informative text features, which are subsequently fed into a multi-layer perceptron for final decision on whether the goods information is accurate. Unlike the recent popular deep architectures, the proposed method leverages the complimentary effect of features from different sources utilizing a wide structure to achieve a superior inspection accuracy. Finally, the proposed method is validated using real JD Logistics data and is demonstrated to outperform the existing techniques. Furthermore, the experimental results also demonstrate that leveraging the complimentary effects can bring additional improvement compared to merely exploring deeper.