# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 4 Challenges and Developments  
Recommender systems are designed to help users discover items that align with their preferences from a wide range of potential recommendations. Deep learning can aid researchers in improving the effectiveness of these recommendations. This paper analyzes four distinct types of recommender systems, including content-based recommendation, sequential recommendation, cross-domain recommendation, and social recommender systems. Although the integration of these recommendation techniques with deep learning has achieved satisfactory results, challenges remain [150]. Future research efforts can be pursued in the following areas. (1) Security enhancements are needed  
With the growth of networking sites, accurately recommending items of interest to users has become one of the key strategies employed by various websites to attract users. Only by digging deep into multidimensional user data can recommendations be identifiedthat truly align with user preferences [151]. In reality, while users expect recommender systems to suggest items of interest, they do not want their other private details to be disclosed [152]. Current research primarily involves distorting and obfuscating user data to ensure privacy [153]. Although this data perturbation strategy does protect the user's personal information, it can lead to the inaccurate extraction of user data, significantly compromising the resulting recommendation accuracy. Therefore, future research could focus on a methodology that not only safeguards user privacy but also enhances recommendation precision. (2) Methods for extracting user preference features are lacking  
At present, recommendation subjects rely heavily on users' ratings or feedback on recommended items. Current research lacks adequate modeling methods that can multi-dimensionally extract user features and recommended items [154], as well as their linear and non-linear relationships [155]. Although neural networks can address this issue to some extent, there remains significant room for improvement. Consequently, forthcoming research should incorporate a more diverse range of methods to extract the features of both users and recommended entities. (3) Evaluation metrics are singular  
When evaluating the performance of recommender systems, the existing studies primarily emphasize the accuracy of results and associated precision rate, considering accuracy as the key metric for determining the effectiveness of recommender systems. However, when users interact with these applications in real-world scenarios, they not only expect the system to provide accurate recommendations for items of interest but also seek a broader and more innovative range of suggestions [156]. Hence, the novelty and diversity of the output recommendations should be considered in the future. In summary, in addition to the aforementioned aspects, there are several directions for future research. There may be an increased focus on the interpretability of deep learning models. This is because, in practical applications, there is a growing demand from users and regulatory bodies for greater transparency and comprehensibility in recommender system decisions. With the proliferation of multimodal data (text, images, audio, etc. ), future recommender systems may integrate these data sources more extensively to provide a richer and more diverse recommendation experience. The application of reinforcement learning in recommender systems may become more widespread, optimizing recommendation strategies via user interactions for higher long-term returns. Future research may explore how to transfer deep learning models from one domain to another to enhance the generalizability of recommender systems. Federated learning, while protecting user privacy, allows different institutions and platforms to collaboratively train recommendation models and may become an important direction in future recommender system research.