This survey provides a comprehensive review of deep learning-based recommender systems, highlighting their growing importance in the field. Recommender systems help users navigate vast information by personalizing recommendations. Deep learning has gained traction due to its ability to learn complex feature representations and improve recommendation quality. The survey categorizes deep learning-based recommendation models into two main types: those using neural building blocks and those using deep hybrid models. It discusses various deep learning techniques, including MLP, AE, CNN, RNN, RBM, NADE, AM, AN, and DRL, and their applications in recommendation systems. The survey also addresses the advantages of deep learning, such as nonlinear transformation, representation learning, sequence modeling, and flexibility. It highlights the challenges and limitations of deep learning, including interpretability, data requirements, and hyperparameter tuning. The survey presents state-of-the-art models, such as Neural Collaborative Filtering, Deep Factorization Machine, Wide & Deep Learning, and Deep Structured Semantic Model, and discusses their performance and applications. The survey concludes that deep learning has revolutionized recommender systems, offering significant improvements in recommendation quality and enabling more complex and accurate models. The survey also identifies future research directions and challenges in the field.This survey provides a comprehensive review of deep learning-based recommender systems, highlighting their growing importance in the field. Recommender systems help users navigate vast information by personalizing recommendations. Deep learning has gained traction due to its ability to learn complex feature representations and improve recommendation quality. The survey categorizes deep learning-based recommendation models into two main types: those using neural building blocks and those using deep hybrid models. It discusses various deep learning techniques, including MLP, AE, CNN, RNN, RBM, NADE, AM, AN, and DRL, and their applications in recommendation systems. The survey also addresses the advantages of deep learning, such as nonlinear transformation, representation learning, sequence modeling, and flexibility. It highlights the challenges and limitations of deep learning, including interpretability, data requirements, and hyperparameter tuning. The survey presents state-of-the-art models, such as Neural Collaborative Filtering, Deep Factorization Machine, Wide & Deep Learning, and Deep Structured Semantic Model, and discusses their performance and applications. The survey concludes that deep learning has revolutionized recommender systems, offering significant improvements in recommendation quality and enabling more complex and accurate models. The survey also identifies future research directions and challenges in the field.