# A Deep Learning-Based Course Recommender System for Sustainable Development in Education
## 2 Related Work  
### Traditional Recommender System  
The development of internet technology has increased the available amounts of several types of data and has resulted in an information overload problem. Therefore, recommender systems are widely used in various application domains to help users find their favorite items or services [1; 13; 16]. Recommender system techniques and algorithms have been proposed in various fields, such as information search, marketing, education, and economics [13; 17], which means they are exciting and valuable for both the academic and business worlds [13; 18; 19]. They have become increasingly popular since the mid-1990s, and they provide solutions to information overload problems on online platforms [20]. In a previous study, recommender systems were divided into Content-based Filtering (CBF) methods and CF-based methods [17]. CBF is a traditional approach that is applied when solving information overload problems [21; 22]. It has been used for analyzing item contents and the similarities between items and user preferences to recommend suitable items. Consequently, this method has an overspecialization issue, in which items similar to those previously purchased are recommended [23; 24]. The CBF approach recommends similar content to that which the user liked in the past, and unlike the CF approach, it does not make recommendations that utilize other users' preferences. The recommendation process of CBF involves finding similarities between user profiles and item features. This method can solve the first-start problem because items with the preferred item attributes are recommended to users. Another approach, CF, is a widely utilized technique in recommender system studies [13]. The idea of this approach is to calculate the similarities between neighborhoods and match users with relevant preferences to provide recommendations [25]. CF approaches are mainly divided into memory-based and model-based approaches [26; 27]. Memory-based CF approaches are subdivided into user-based collaborative filtering (UCF) and item-based collaborative filtering (ICF) [17]. The UCF approach compares the ratings of the same items to calculate similarities between users. The item prediction for a given user is calculated as the weighted average of all users' evaluations of the item. The weights here are the similarities between these customers and the target items [13; 17]. Conversely, the ICF approach uses the similarities between items to calculate predictions [28]. Model-based CF approaches utilize machine learning or data mining techniques to enhance model performance [13]. The strength of these approaches is that they use pretrained models and can quickly make recommendations. Additionally, they have proven capable of making recommendation results similar to those output by neighborhood-based recommendation techniques [28]. Typical model-based approaches include dimensionality reduction techniques such as singular value decomposition (SVD), regression, and clustering [3; 17]. A model-based approach utilizes user-item interactions to capture the relationships between items and solve some of the sparsity issues associated with recommender systems [17]. Despite the success of CF approaches, some problems have been revealed. Such an approach essentially makes suitable recommendations based on users' preferences. However, recommender systems experience cold-start problems when encountering new users, as there are insufficient data for measuring similarities. Therefore, these systems cannot predict users' preferences [17]. Furthermore, there is a first-start problem in which users' preferred items cannot be recommended because they have not yet been purchased [17]. There is also a scalability problem that slows down a CF algorithm's computation process as the data become increasingly extensive.