## 1 Introduction  
Recommender systems have long attracted attention and have become increasingly essential in many aspects of our lives. An effective recommendation algorithm benefits both the user and service (or content) provider. If the recommendation system can reasonably match the product, service, or content with what a user wants, high user satisfaction and sales growth for the provider can be guaranteed. A knowledge graph (KG) is a database with a graph structure that can express various relationships [1]. In many machine learning algorithms, KGs have been used as an effective tool to provide explainability to their results [2]. Owing to their reasoning capability and human-readable characteristic, recommendation is also one of the areas where the use of KGs has received significant attention [3]. However, devising a method to process KGs for real-world recommendation applications is challenging because of their complex graph structure. With the recent progress in deep learning technology, recommendation methodologies using KG-embedding techniques have been proposed [4, 5]. Despite their high performance, the explainability of KGs is erased in such methodologies, making it difficult to provide meaningful rationale for recommendations. Graph-learning-based algorithms [6, 7] have also been proposed, but they require relatively large computing resources for an iterative propagation process. In this study, we propose a user profiling method that represents user preferences while maintaining the semantic information inside the KG using frequent subgraph mining (FSM). Instead of directly embedding the KG, we identify the user-preferred attributes from frequent subgraphs and then use them to create vectors representing users and items. This user profiling method is rather simple but powerful; by training user-item representation pairs in a neural network, we obtained decent recommendation performance while still being able to describe user preferences with the user profile. First, we combined a KG representing metadata about items and user-item interaction data to create an expanded KG. Considering the range of information to be used for the recommendations, this KG was divided into graphs for each user. Subsequently, we extracted frequent subgraphs, which explicitly represented the preference of each user. The set of subgraphs was used to create user profiles, which are vectors containing both the semantic information of KG and frequency from the user-item interaction data. In other words, the user profile represented both the common attributes of the items interacted with by each user and the intensity (i.e., frequency) of the preference of each user for these attributes. By determining the dimension of the user profiles as the number of attributes appearing in the frequent subgraphs of all users, we could explain the preference of each user with respect to the preference space of the entire user group. After obtaining the user profiles, we sampled positive and negative pairs of user-item interactions. We then fed the user profile and item vector pairs into a neural network classification model and trained the model to learn their relationship to recommend a specific item to the target user. Owing to the powerful approximation ability of the neural network, our methodology demonstrated good recommendation performance while providing concrete reasons for the recommendation based on the information in the user profiles. To verify the performance of the proposed profiling method, we conducted experiments using movie data. The movie data have been used in previous studies [8; 9; 10] owing to having many attributes in a single item; relatively more people are involved in making a film than writing a book or piece of music. As filtering out only the meaningful attributes is essential for recommendation in the movie domain, it is an appropriate domain to examine the effectiveness of our profiling method, wherein user-preferred attributes are determined through FSM.