# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems
### Cross-Domain Recommendation
#### 3.3.1 Single-Target CDR  
Single-target CDR is the conventional approach in the CDR domain, and most CDR methods primarily focus on this scenario. It involves recommending items to users in the target domain by utilizing information acquired from the source domain. Single-objective CDR methods predominantly focus on transferring valuable knowledge.  
There are also various recommendation methods, it initially establishes connections based on common content elements such as user/item attributes [119], 'like' data, and browsing or viewing histories [120]. Subsequently, they employ these connections for the cross-domain transfer of user/item data or knowledge. Embedding-based transfer methods often project instances from both the source and target domains into a novel feature space, where instances from both domains display similarities. Other methods often adopt distinct techniques or philosophies, including Bayesian latent factor models, interest drift, ternary relations, and reinforcement learning. Rating pattern-based transfer methods typically begin by learning the distinct rating patterns of users from the source domain. Subsequently, these patterns from the source domain are harnessed to enhance the model's recommendation performance in the target domain, facilitating knowledge transfer. [PERSON] et al. and [PERSON] et al. [121] both focused their efforts on leveraging social networks to enhance recommender systems. While the former harnessed Facebook's friend relationships to reinforce user models in the target domain, the latter delved deeper, em  
Figure 6: Process of the cross-domain recommender system.  
ploying random walk algorithms to extract latent user information within social networks. These approaches can be perceived as endeavours to uncover both explicit and implicit user relationships within the constructed network.  
Subsequently, [PERSON] et al. [122] implemented a semisupervised transfer learning methodology, exploring the user interest similarities between the source and target domains, thereby indicating the transferability of user interests across different domains. Their research contradicts the traditional belief, suggesting that even with a small number of overlapping users, valuable information can be provided for the entire system. By extracting and utilizing information from overlapping user groups, semisupervised transfer learning approaches can be employed to deal with the issue.  
However, these studies also highlighted challenges, emphasizing that the effectiveness of the recommendation process heavily relies on the extent of user overlap across different domains. This identified limitation provides a direction for subsequent research, specifically on how to deliver effective recommendations even when the user intersection is minimal. To exploit these latent and obscured relationships, studies have increasingly pivoted towards multidomain recommendation (MDR). Compared to single-target CDR, the focus of MDR primarily lies in handling recommendations across different domains. Although multidomain methods can be applied within the context of CDR, their scopes in practical applications often face certain constraints. To address data sparsity and the other challenges inherent in multidomain recommendation scenarios, [PERSON] et al. introduced MCF. Successive studies [123, 124, 125] integrated more advanced techniques such as feature fusion aiming to further enhance the accuracy of the output recommendations.  
In summary, from harnessing both explicit and implicit information within social networks to the transfer of interests across domains and onwards to the exploration of multidomain recommendations, researchers have continuously driven to identify more effective and universal recommendation methods that can cater to user needs across various domains and scenarios.