# A Deep Learning-Based Course Recommender System for Sustainable Development in Education
## 1 Introduction  
The development of internet technology has increased the available amounts of several types of data and has resulted in an information overload problem [1, 2]. Recommender systems are widely utilized in internet applications to help users find their favorite items or services when they face too many options or too much information [3]. Recently, in preparation for the post-COVID era, the global education market has accelerated its transition from offline education to online education [4, 5]. As the online education market proliferates, online course websites such as edX, Coursera and K-MOOC are becoming widespread and seeing increasing numbers of subscribers. Therefore, the range of course-related information available to users is increasing rapidly [6]. Several studies show that users face difficult situational problems due to the large amount of information available when choosing a course on an online education website [7, 8]. This selection process is challenging and time-consuming when choosing their preferred course on such websites. The valuable information provided by a course recommender system can include relevant resource information such as job opportunities and the users' interests. Therefore, course recommender systems on online education websites must utilize diverse resources to match each user's individual goals, interests, and knowledge structure [9]. The process of user choosing personalized course can be highly challenging and complex. Above all, as of recently, the user can easily find information related to courses on an online education website [10]. However, the availability of more course information on anonline education website does not mean that users can choose personalized courses [11]. Instead, users may face the challenge of too many alternatives and information overload problems when using such websites [9]. Deep learning techniques have recently received much attention from several domains due to their higher performance than that of traditional techniques. Many studies have recently conducted deep learning approaches on recommender systems to enhance their performance [12]. However, few studies make personalized recommendations that utilize comprehensive information about the relevant online education domain. One of these studies' motivations is to help users for finding and choosing their preferred courses. Much course information continues to be uploaded on online education websites, and therefore, the recommendation of personalized courses for users is a significant challenge. Many online education websites have built course recommender systems based on traditional data mining approaches, such as Collaborative Filtering (CF), the association rules approach, and the most representative analysis technique [9; 13; 14; 15]. While the previously developed course recommendation approach is highly effective and useful, it possesses certain significant limitations. First, a previous study has shown that recommender systems based on memory-based models that utilize user-item interactions do not meet individual users' personalized requirements. The most popular memory-based approach (e.g., CF) predicts user preferences from historical actions such as clicking, rating, and viewing. However, this approach only models the action itself without understanding the user behavior motivation. Second, the traditional approach has cold-start problems, where new users lack past data that could be used by the system to make recommendations. Another issue is the first-start problem by which a system cannot make recommendations until a user provides a preference rating. Another limitation of the previously developed recommendation approach is that it does not consider comprehensive information about the user's most relevant courses. For example, users choose courses based on information such as subjects, future careers, and certificates. Categorizing users' individual needs and areas of interest can recommend appropriate courses.