Enhancing Customer Service Recommendation: A Hybrid Model Incorporating Factorization Machine with Temporal & Cross Networks and Behavior Sequence Analysis

This paper presents a novel approach to address the challenges of personalization and efficiency in customer service within the telecommunication industry. The proposed model utilizes behavioral sequences and employs deep learning techniques to develop a customer service recommendation system. By leveraging the temporal dependencies in these sequences, the model accurately predicts the likelihood of customer clicks on various recommended items, thereby enabling the delivery of personalized customer service solutions. To enhance recommendation accuracy and effectiveness, the model integrates several key technologies, including input-aware factor machines, inflated causal networks, and feature crossover networks. These technologies effectively capture associations and higher-order interactions among features, resulting in improved recommendation outcomes. Experimental validation of the model demonstrates significant performance enhancements in customer service recommendation tasks. Overall, the proposed model provides a promising solution for enhancing personalization and efficiency in customer service within the telecommunication industry. Telecom customer service recommendation success rate prediction and click-through rate prediction both belong to binary classification tasks. Experimental results have demonstrated that the model performs excellently in both customer service recommendation and click-through rate prediction tasks.