Knowledge Graphs and Neural Networks in Recommendation Systems: A Comprehensive Survey and Future Directions

Recommendation systems are crucial components of modern digital platforms, enhancing user experience and driving engagement by providing personalized suggestions. Traditional recommendation models, such as collaborative filtering and content-based filtering, have been widely adopted but face significant challenges, including the cold-start problem, data sparsity, and limited interpretability. Recent advancements have explored the use of knowledge graphs (KGs) and neural networks to address these limitations. Knowledge graphs provide a rich semantic framework for representing user and item relationships, thereby enhancing the recommendation quality by uncovering latent connections. Neural networks, particularly deep learning models, have demonstrated their capability to learn complex patterns from large datasets, resulting in improved recommendation accuracy and personalization. This survey provides a comprehensive overview of the integration of knowledge graphs and neural networks in recommendation systems. It reviews the evolution of recommendation models, from traditional methods to more sophisticated approaches that leverage KGs and neural architectures, such as Graph Neural Networks (GNNs) and Neural Collaborative Filtering (NCF). The paper compares these models based on scalability, accuracy, interpretability, and computational complexity. It identifies key research gaps, including scalability issues, dynamic adaptation of KGs, and privacy concerns, and outlines future research directions to address these challenges. By exploring novel hybrid approaches that combine the strengths of knowledge graphs and neural networks, this survey aims to advance the development of more robust, explainable, and scalable recommendation systems.