# A Comprehensive Survey of Recommender Systems Based on Deep Learning
###### Abstract
Second, deep learning models can handle large-scale data and are therefore able to cope with information overload. In addition, personalized recommendations that take advantage of potential representations of users and items can solve the cold-start problem to some extent. In addition, deep learning can improve the robustness of recommender systems and improve user experience. The reason why deep learning is so captivating lies in its effectiveness in addressing numerous challenges encountered by recommender systems. First, deep learning models can learn intricate features of users and items, allowing for a more precise capture of subtle variations in user behavior. Second, deep learning models can handle large-scale data, thus mitigating the issue of information overload. Furthermore, personalized recommendations based on the latent representations of users and items can, to some extent, alleviate the cold-start problem. Additionally, deep learning can enhance the robustness of recommender systems, improving the user experience. In this study, we provide a comprehensive overview of recommender systems with a particular focus on their integration with deep learning. We introduce various types of recommendation methods, technological trends, and application domains that leverage deep learning. We offer detailed descriptions from four perspectives: content-based recommendation, sequence recommendation, cross-domain recommendation, and social recommendation. Additionally, we delve into the application of deep learning in recommender systems, including model principles, performance enhancements, and application cases,with the aim of providing readers with a deep understanding of this field. Furthermore, we discuss potential future trends in deep learning-based recommender systems, including model interpretability, multi-modal recommendation, privacy, and fairness considerations. The primary contributions of this paper can be summarized as follows. * We present a comprehensive examination of recommender systems, with a specific emphasis on their integration with deep learning. We categorize them in terms of their developmental perspective, providing a comprehensive view of the evolution of the recommender systems field. * We conduct a review of the research progress of recommender systems integrated with deep learning, focusing on methods for applying deep learning to collaborative filtering. Specifically, we perform a comprehensive analysis of four recommendation approaches that incorporate deep learning: content-based recommendation, sequence recommendation, cross-domain recommendation, and social recommendation. * We identify future research directions in the field of deep learning-based recommender systems, contributing to the advancement of the research community. Throughout this review study, in comparison to existing literature, we have pinpointed articles that excessively concentrate on content-based recommendation and collaborative filtering, with relatively less attention given to reviews concerning the direction of deep learning. Consequently, the following gaps have been identified: Despite the exceptional performance of deep learning models, their interpretability remains a challenge. This paper aims to explore ways to improve the interpretability of models to meet the requirements of both users and regulatory authorities. Future research could delve deeper into methods for enhancing model interpretability to bolster user trust. With the proliferation of multi-modal data, recommender systems need to better integrate diverse types of information. We believe that future research can explore methods for the more profound fusion of multi-modal data. Recommender systems should also make strides in addressing user privacy concerns and guaranteeing fairness in recommendation results. Future research should focus on how to protect user privacy and ensure fairness while providing personalized recommendations.