## 2 Materials and Methods
### Recommendation Algorithms
Existing KG-based recommender systems can be generally categorized into three classes: embedding-based, path-based, and unified (or graph neural network-based) methods [6]. Embedding-based methods use various types of information from the KG to model the representations of items or users. Representative methods [4; 5] characterize items through graph embedding algorithms such as TransE [19] or TransR [20] and apply them for recommendation. Although these methodologies show a good performance, because of the characteristics of the embedding process, they suffer from the shortcoming of losing the semantic information in the original KG. In other words, as entities are converted into numerical vectors, the semantic information originally represented as paths in the KG is lost. Path-based methods consider the pre-defined connecting patterns or meta-paths of users and items, leveraging the similarity of paths between entities to enhance the performance of the recommendation. [PERSON] et al. [21] suggested HeteMF, which focused on the relations of items connected by meta-paths to calculate item-item similarity. Drawing from the collaborative filtering approach, [PERSON] et al. [22] proposed the HeteCF algorithm using item-item similarity in addition to user-item and user-user similarities. Although the meta-paths can introduce explainability in recommendations, there is a downside; the effectiveness of these methods depends heavily on the quality and amount of manually created meta-paths, which requires expertise in the target domain. This reduces their applicability to other domains. Graph neural network (GNN)-based methods attempt to obtain better representations by integrating semantic information with connecting patterns through embedding propagation [6]. [PERSON] et al. [7] proposed a KG convolutional network, which iteratively updates the representation of each entity through an inward propagation process from its neighbors. Another method is KGAT [23], which initializes the representation of entities with TransR [20] and propagates information outwardly. Recent GNN-based algorithms are not necessarily used only for KG-based recommendations, but they show high performance with KGs owing to their graph modeling ability. However, they require a relatively large amount of computation compared to other methods. Our proposed method does not clearly fall into any of these three main categories. Methodologies based on KGs that can use additional information about items with stronger explanatory power compared to methods solely based on user-item interactions and are relatively immune to cold start problems. Recent KG-based recommendation methodologies, such as KGAT [23], have shown high performance on data in the entertainment domain (books, music) and business service domain (restaurants). Moreover, they exhibit strengths in domains where there is a high necessity to clearly explain the recommendations, such as in the education domain for recommending educational content and in the medical domain for clinical treatment recommendation scenarios. However, KG-based methodologies tend to focus on utilizing semantic information and often overlook frequency information, which is a crucial element representing user preferences in user-item interactions. Our approach differs from existing methodologies by addressing this issue; we create user profiles by incorporating frequency information. In this paper, we have devised a novel user profiling method that creates user profiles using frequent subgraphs extracted from the graph of each user. A frequent subgraph, consisting of common attributes of user-interacted items with their frequencies, represents user preference well without losing the semantic information of the original KG. Subsequently, we converted the subgraphs into user profile vectors and inputted them into a neural network along with item vectors with the same vector space. The neural network, acting as the function \(f\) in (1), effectively modeled the relationship between users and items to predict preference.