Prediction Method of O2O Coupon Based on Multi-Grained Attention Mechanism of CNN and Bi-GRU

Abstract

O2O (Online to Offline) can analyze users’ behaviors according to data mining, realize personalized marketing and improve marketing effect. The “push” delivery method of O2O coupons ignores the active participation and user experience of users, and the pertinence and effectiveness of delivery are greatly affected. Aiming at the simple structure of a single network and mainly relying on artificial construction to extract features, in order to improve the utilization efficiency of deep-seated features in the model and effectively extract multi-level features, this paper introduces Bi-GRU according to the time-series characteristics of O2O consumption behavior, and proposes a new multi-grained attention mechanism. First, build a multi-dimensional consumer behavior feature project; Secondly, using convolutional neural network (CNN) and Bi-GRU to extract local and global features; Finally, multi-level and multi-grained information is extracted by using multi-grained attention to avoid the loss of hierarchical structure information, enrich feature vectors and further improve model performance. Using real O2O coupons and data sets, the CB-MA model proposed in this paper achieves 93.29% accuracy, 91.72% AUC and 0.0332 Loss. The results show that multi-grained attention mechanism can extract multi-level features more effectively than traditional attention mechanism. At the same time, CNN and Bi-GRU are combined to learn local and global features at the same time, and the correlation information of time and space is extracted.

Keywords

• Attention mechanisms
• Bi-GRU architecture
• Bi-GRU training
• customer purchase patterns
• convolutional neural network
• multi-grained attention mechanism