Enhancing Personalization with Graph Neural Networks in Agile Recommendation Systems

Abstract

Traditional recommender systems often struggle to adapt to rapidly changing user preferences and dynamic contexts. Agile Recommendation Systems (ARS) address this by incorporating real-time learning and context awareness. This survey explores recent ARS advancements, focusing on deep learning techniques for user modeling and context-aware recommendations. It highlights the challenges of evaluating ARS, particularly the lack of explainability and the potential of Graph Neural Networks (GNNs) and Reinforcement Learning (RL).This study proposes a novel ARS framework that combines GNNs, RL, and explainability for personalized and trustworthy recommendations. GNNs capture complex user-item relationships, and RL enables real-time adaptation. Explainability techniques are integrated to enhance user trust by providing insights into the recommendation rationale. It includes real-world situations that demonstrate how these techniques can be applied in practical recommendation systems, making the concepts more tangible and relevant. This research offers: A comprehensive review of ARS advancements and deep learning techniques. A proposed framework with GNNs, RL, and explainability for enhanced personalization and user understanding. A focus on addressing explainability challenges in GNN-RL based systems. A discussion of potential biases within GNN and RL models and mitigation strategies. By leveraging GNNs and Reinforcement Learning with explainability, this ARS framework has the potential to revolutionize the recommendation landscape, delivering more personalized and trustworthy experiences for users, ultimately enhancing user satisfaction , engagement and overall user experience.