همایش بین المللی هوش مصنوعی

صفحه اصلی / اولین همایش بین المللی هوش مصنوعی

Enhancing Telecom Recommendation Systems through Customer Profiling and Graph Neural Networks (GNN) on Graph Data

نویسندگان :

Jaber Alavi¹ Mahmood Neshati²

1- دانشگاه امام رضا مشهد 2- دانشگاه شهید بهشتی

کلمات کلیدی :

Customer Profiling،Graph Neural Networks (GNN)،Recommendation Systems،Telecom Industry،Graph Data،Personalized Services،Collaborative Filtering (CF)،Content-Based Filtering (CBF)،Matrix Factorization (MF)،Customer Interaction Data

چکیده :

Telecommunications companies rely on recommendation systems to deliver personalized services and enhance customer satisfaction. Traditional methods, such as Collaborative Filtering (CF) and Content-Based Filtering (CBF), often fall short in capturing the complex relationships and social influences inherent in large telecom networks. In this paper, we propose a novel Graph Neural Network (GNN)-based recommendation system that integrates customer profiles with graph data representing customer interactions (e.g., calls, messages). The system uses the GraphSAGE architecture to aggregate information from each customer’s network, enabling it to learn from both direct and indirect relationships. By combining customer demographic and usage data with interaction networks, our model provides more accurate and personalized service recommendations. We evaluate the system on a real-world telecom dataset, comparing it with traditional models, including CF, CBF, and Matrix Factorization (MF). The GNN-based system achieves a significant performance boost, with a precision of 0.81 and an F1-score of 0.80, outperforming all baselines. These results highlight the ability of GNNs to capture social and communication patterns, making them highly effective for telecom recommendations. Future work will explore the scalability of the system and its application to real-time data, further enhancing its potential for customer retention and revenue growth.