# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems
### Content-Based Recommendation  
Content-based recommendation is the earliest and most basic type of recommendation algorithm, and it has played a pivotal role in the entire history of recommender systems [54]. Although its effect may not be as good as that of new recommendation algorithms, it is still very valuable and even essential. Content-based recommendation algorithms form a relatively intuitive category of algorithms that primarily offer recommendations by comparing item features with users' historical preferences. In the early days, it relied primarily on specific content, such as the characteristics of text, images, or audio. Their approach entails suggesting items to users that exhibit content similarity to previously preferred items, determined by the historical preferences of these users [55; 56]. A content-based recommendation model is constructed based on the target item, user information, and interactions between the user and items. Importantly, it notably neglects the actions of other users. A schematic process is shown in Figure 2 below. To achieve content-based recommendation, the general steps include (1) constructing an item feature representation based on the content features of each item; (2) building a user feature representation based on user characteristics and behaviors; and (3) generating a recommendation list according to the level of alignment between the features of the target item and those of the user [57]. When integrated with neural networks, the entire recommendation model can be divided into an input layer, a hidden layer, an output layer, and a prediction layer. A content-based recommendation model is shown in Figure 3.  
The hidden layer is a fully connected layer consisting of 1-2 sublayers, and the score of the item is output [58]. This model highlights the embedding and transformation of item features. The transfer process from the input layer to the next is represented as shown in Equation (2). \[h=f\ (\ W_{ih}x+b_{h}) \tag{2}\]  
The transfer process between hidden layers is depicted as shown in Equation (3) (if multiple hidden layers are present). \[h_{j}=f(W_{hj}\ h_{j-1}+b_{j}) \tag{3}\]  
The transfer process to the output is shown in Equation (4). \[y=f(W_{ho}\ h+b_{o}) \tag{4}\]  
where x represents the input and y signifies the output. w and b are parameters, while f is a nonlinear activation function. Figure 3: Content-based recommendation procedure. Figure 2: Diagram of a content-based recommendation algorithm. The process of representing items and users via tagging is becoming increasingly popular. Tags can serve as content information and be used in content-based recommendation methods [59], or they can be treated as a third entity, utilizing collaborative filtering approaches for recommendation. However, as data representations become more diverse, the preprocessing tasks for items become increasingly challenging. A one-dimensional vector representation can be overly simplistic, thereby neglecting the intrinsic relationships between various attributes of the given content. A tensor is a multidimensional data structure that is capable of representing the co-occurrence relationships of multiple types of attributes. Tensor decomposition is fundamentally a feature extraction method, and many algorithms that leverage tensor decomposition for model construction have been developed in the past. [PERSON] et al. [60] constructed a tensor for user-item-tag triples and employed Tucker decomposition to obtain abstract representations of users. The core tensor obtained from the decomposition procedure encapsulates the associations between different attributes, thereby taking the cooccurrence relationships between items and tags into consideration. Unlike traditional methods such as prod2 vec, which typically only considers user interactions to create embeddings, Meta-prod2 vec not only utilizes tags but also incorporates additional information, such as product descriptions and reviews, to generate richer product embeddings [61].