Developing Recommendation Systems Using Deep Learning: Comparison of Models and Directions for Improvement

Recommendation systems have become increasingly important in various domains, aiming to provide personalized suggestions to users. With the advent of deep learning, there has been a significant advancement in developing more accurate and efficient recommendation systems. This study presents a comprehensive comparison of popular deep learning models used in recommendation systems, including Multilayer Perceptron (MLP), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, and Graph Neural Networks (GNNs). We evaluate these models using well-established evaluation metrics and datasets, and discuss their strengths and weaknesses in different recommendation scenarios. Furthermore, we identify key challenges and directions for improvement, such as addressing the cold-start problem, enhancing scalability, incorporating context and user preferences, and improving explainability and interpretability of recommendations. We also explore future trends and opportunities, including the integration of deep learning with other techniques, multimodal and cross-domain recommendations, and emerging application areas. Our findings provide valuable insights for practitioners and researchers in developing more effective and user-centric recommendation systems using deep learning techniques. This study contributes to the advancement of recommendation systems and highlights the potential for further research and innovation in this field.