# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 2 Related Work  
Recommender systems are derived from advances in cognitive science, approximation theory, information extraction, and prediction theory. They have undergone rapid evolution since their birth and have become an important catalyst for the rise of the modern business economy [16]. These systems filter out redundant information from vast amounts of data, selecting items that can fulfill latent user needs. Traditional recommender systems typically focus on whether users have shown interest or rated an item [17]. By analyzing historical interaction data, these systems uncover the underlying demands, thereby achieving efficient recommendations. Recommender systems are fundamentally based on exploiting binary relationships between users and items. By utilizing historical behaviors or similarity relations, these systems help identify items that might pique users' interests [18]. A utility function, denoted as \"s\", is employed to calculate the recommendation score for item \"i\" with respect to user \"u\". In this context, both users and items are characterized by a collection of distinct attribute features [19]. By calculating recommendation scores, the aim is to find the most interesting i\({}^{\prime}\in\) I for every u \(\in\) U, as shown in Equation (1). \[\forall\text{u}\in U,i_{u}^{\prime}=\text{argmax}s(u,i) \tag{1}\]  
In 2008, [PERSON] et al. from the University of Hong Kong [20] took an innovative step by integrating user social interaction information with historical item rating data. This was a pioneering effort that introduced the SoRec recommendation algorithm, which was built on the foundation of probabilistic matrix factorization (PMF) [21]. Recognizing the increasing significance of social interactions in recommendations, research in this area witnessed exponential growth. In 2010, [PERSON] [22] delved deeper into the sphere of trust propagation among users and their social friends. They adeptly combined matrix factorization techniques with interactive trust, leading to the conception of the Social MF model. A year later, in 2011, building on the foundation laid by Social MF, [PERSON] et al. utilized social regularization for recommendation [23]. This model was predicated on the assumption that the greater the influence of neighboring friends on a user, the more the user's latent preferences would converge towards that friend. When it comes to neural networks, [PERSON] et al. of Australia's NICTA research center ingeniously applied an autoencoder model to recommender systems in 2015, giving birth to the single-hidden-layer neural network recommendation model known as AutoRec [24]. Using these operations, AutoRec has generalization and expression capabilities and it is the basic application of deep learning in this field. Pushing the boundaries further, [PERSON] and [PERSON] [25] achieved enhanced short-term prediction accuracy in 2017 by redefining collaborative filtering (CF) as a sequence prediction problem using neural networks. In summary, the evolution of recommender systems, as seen over the years, has seamlessly transitioned from leveraging social interactions to harnessing the prowess of deep neural networks. This evolution trend underscores the dynamic nature of research in this domain and the search for more refined and accurate recommendation techniques. Graph neural networks (GNNs) form a collective term for models that apply neural networks on graphs, which have powerful graph-structured data learning capabilities. Graphs consist of nodes and edges connecting nodes and are typical non-Euclidean spatial data structures. Social networks are quintessential examples of graph data. Using GNNs, social network graph models can be seamlessly integrated into recommender systems. Hence, research on social recommendation has progressively become a focal point.