## 2 Materials and Methods
### Recommendation Algorithms  
The purpose of a recommender system is to match users to items they may like based on their preference inferred from behavioral data (i.e., user-item interactions) [11]. Recommendation problems can be described using the following equation:  
\[\hat{y}_{ui}=f(v_{u},v_{i}). \tag{1}\]  
The predicted preference of user \(u\) for item \(i\), denoted as \(\hat{y}_{ui}\), can be calculated using the function\(f\) of user representation \(v_{u}\) and item representation \(v_{i}\)[12]. Finding both the function\(f\) and a convenient way of representing users and items to make \(\hat{y}_{ui}\) close to the actual preference \(y_{ui}\) is at the core of item recommendation. Depending on the type of value \(y_{ui}\), recommendation algorithms can be divided into two categories: implicit and explicit feedback-based [13]. Implicit feedback is based on whether a user has interacted with an item. Therefore, implicit feedback-based algorithms often attempt to solve binary classification problems and use accuracy and F1-scores as their performance metrics. By contrast, explicit feedback-based algorithms use more direct responses given by the user, such as user ratings. They view the recommendation task as a multi-class classification or regression problem to predict ratings and make recommendations if the predicted rating is high. For regression problems, metrics such as root mean square error are used. In this study, we proposed an algorithm based on implicit feedback; predicted preference \(y_{ui}\) is the probability that user \(u\) interacts with item \(i\). In addition, recommendation algorithms are divided into three categories based on their filtering types: collaborative, content-based, and hybrid filtering [13; 14]. Collaborative filtering primarily considers the similarities between users or items in user-item interaction data. Since the underlying assumption is that users with similar preferences select similar items, this technique is domain independent. Various methods, such as clustering techniques [15] or matrix factorization [16], have been proposed. Content-based filtering uses domain-specific information to solve data sparsity and cold-start problems [10]. This additional domain-specific information varies from text or images about items to the demographic information of users, through which users and items are represented for recommendation. Finally, the hybrid method attempts to achieve higher performance by combining multiple approaches [17]. Figure 1: Overview of proposed method. Note: DNN, deep neural network. Our proposed method involves hybrid filtering; it benefits from the characteristics of content-based filtering in that it creates user profiles using a KG as external information but also resembles collaborative filtering in that information from multiple users is reflected together in user profiling. KG-Based Recommendation Algorithms  
In recent years, many researchers have introduced the KG as additional information to recommender systems [6]. The term \"KG\" has never been clearly defined with a single meaning; however, it can generally be understood from the perspective of Semantic Web technologies or Linked Open Data [1]. Based on this perspective, we used common elements and concepts such as TBox and ABox levels, schemas, classes, object properties, datatype properties, and instances, which are defined in W3C standards such as the Resource Description Framework (RDF), RDF Schema, and Web Ontology Language. Generally, a KG can be defined as follows:  
\[KG=(V,E)=\{(v_{1},e,v_{2})|v_{1},v_{2}\in V,e\in E\} \tag{2}\]  
where \(V\) is the set of vertices constituting the graph, \(E\) denotes the set of edges constituting the graph, and \((v_{1},e,v_{2})\) refers to a unit that forms a graph called a triple. One of the key benefits of KG-based recommendation is the explainability when recommending items [18]. Many attempts have been made to use the semantic information and expressiveness of KGs [3; 4; 5].