With the continuous growth of user behavior data on e-commerce platforms, effectively predicting user behavior, providing personalized product recommendations, and addressing the cold-start problem have become key challenges in recommendation systems. To address these issues, this paper proposes a novel customer relationship management model, TFP-Net, which integrates Temporal Graph Attention Mechanism (TGAT), Deep Feature Interaction Module (DeepFM), and Prototypical Network (ProtoNet) to enhance the performance of recommendation systems. Specifically, TFP-Net uses TGAT to capture the temporal features of user behavior, handling the dynamic changes in user actions across different time periods. The DeepFM module learns the complex non-linear relationships between users and products, while the ProtoNet optimizes recommendations in cold-start scenarios, mitigating the data sparsity issues for new users and products. In the experiments, we evaluate the model on the Taobao User Behavior Dataset and the Amazon Product Dataset. The results demonstrate that TFP-Net outperforms traditional baseline models on both datasets, especially in cold-start scenarios, with a performance improvement of 1.5% to 2.1% over the best existing models, namely DGN-JBP on the Taobao dataset and GACE on the Amazon Product Dataset. TFP-Net achieves an accuracy of 87.8% on the Taobao dataset and 88.1% on the Amazon dataset. Additionally, the model demonstrates superior computational efficiency, with lower training time and inference latency compared to other models, proving its potential in large-scale data processing. TFP-Net effectively addresses user behavior prediction and product recommendation on e-commerce platforms, excelling in cold-start scenarios and computational efficiency. It provides a new solution for personalized recommendations and customer relationship management.
Li et al. (Thu,) studied this question.