# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems
### Cross-Domain Recommendation  
In the preceding sections, our discussion was primarily focused on generating recommendations based on users' historical behaviors and content characteristics, usually within a single application scenario or domain. However, to reap benefits from a variety of services, users frequently engage with multiple social media platforms [110]. Cross-domain recommendation (CDR) has been introduced to utilize richer information from multiple domains, aiming to improve recommendation performance. Studies indicate strong correlations between user-generated data across different domains [111]. Traditional data mining techniques are primarily designed for single-domain analysis, and by overlooking data from other domains, they often encounter issues related to data sparsity [112]. Consequently, addressing the utilization of cross-domain data to enhance the comprehensiveness and accuracy of recommendations has become a prominent research focus. By aggregating data from different domains, CDR methods not only compensate for the missing information in a single domain but also holistically harness the value embedded in social media data. The framework is shown in Figure 5 below. As depicted in Figure 5, the input layer is designed to accommodate user behavior and item attributes from disparate domains. After performing feature extraction, the data acquired from these diverse domains are aggregated into a shared hidden layer, which is tasked with capturing cross-domain patterns and relationships. Within the hidden layer, neural networks can be employed to integrate information from various domains. By employing this design, the model can generate an output layer that synthesizes information from multiple domains, thereby facilitating more comprehensive and accurate recommendations. Cross-domain recommender systems aim to improve recommendation performance [113] by utilizing data from various domains, particularly in scenarios with data sparsity or cold start challenges. These systems take users' behaviors and preferences across different domains into account, harnessing comprehensive multidomain information to provide a more holistic user profile and, in turn, bolster recommendation accuracy. Cross-domain recommender systems exploit multidomain learning and knowledge transfer [114], diversifying their recommended content to satisfy the needs of varied scenarios and domains. However, multidomain data processing and the associated knowledge transfer step may also result in increased computational costs. Traditional recommender systems grapple with two main challenges: the cold start problem and data sparsity [115]. CDR methods offer an effective remedy for these issues by analyzing user interactions from other domains to capture user preferences from specific perspectives. This can be employed to enhance data in the target domain or supplement information during the initialization process for new users. While traditional recommender systems build and analyze a recommendation model within the current domain, cross-domain recommendations necessitate deciding which information should be transferred between distinct domains and the means of performing such transfers. Cross-domainrecommendation approaches can harness user interaction data from other domains for auxiliary analysis purposes, capturing certain user preferences. This enables enhanced recommendations to be produced in the target domain or even across multiple domains and provides additional information for new user initialization, thereby addressing the two challenges that are endemic to traditional recommender systems. In recent years, researchers have focused on addressing data sparsity and cold-start issues in the target domain for cross-domain recommendation. They have conducted extensive studies on deep learning-based cross-domain recommendation methods. These approaches consider deep auxiliary information, including neighboring domain descriptions and rating information [116; 117; 118].