Personalization Driven by Artificial Intelligence Powered by customer interactions and Revolutionized Digital Commerce Management
Abstract
Artificial Intelligence (AI)-driven personalization, fueled by customer interactions, is transforming Digital Commerce Management by delivering tailored experiences that enhance engagement and conversions. The integration of AI in recommendation systems has significantly improved user satisfaction and business outcomes. However, traditional recommender systems face challenges such as data sparsity, cold-start problems, and an inability to effectively capture long-range dependencies in user behavior. These limitations result in suboptimal recommendations that fail to adapt dynamically to evolving user preferences. To address these issues, we propose the Transformer-Based AI Recommender Systems (T-AIRS) framework. T-AIRS leverages self-attention mechanisms to model complex user-item interactions, enhances contextual understanding through deep sequence learning, and incorporates reinforcement learning for adaptive personalization. By utilizing advanced embeddings and multi-modal data fusion, T-AIRS effectively mitigates coldstart problems and sparsity issues. The proposed method is implemented in Digital Commerce Management platforms to analyze user behavior, predict preferences, and provide real-time, hyper-personalized recommendations. This enhances customer engagement, retention, and sales performance. Experimental results demonstrate that T-AIRS outperforms conventional recommendation models by significantly improving recommendation accuracy, diversity, and adaptability. The findings suggest that AI-driven personalization, powered by transformer-based techniques, revolutionizes Digital Commerce Management by delivering highly relevant and dynamic user experiences.