# A Deep Learning-Based Course Recommender System for Sustainable Development in Education
## 1 Introduction
It can help users choose personalized courses by integrating different data features and developing establish valuable course-related information. This is a motivation to develop a novel approach to overcome overload information and gain comprehensive knowledge of recommended items. The following research issues need to be addressed: First, integrate all available information about the course (e.g., job opportunities, user interests) and build relationships between the relevant information. Second, it needs to use all integrated information to recommend personalized courses that best suit users' individual needs. Therefore, this study proposes a novel deep learning-based course recommender system (DECOR) model that can elaborately extract high-level user behaviors and course attribute features. The DECOR model can help users find their preferred courses, solve high-dimensional sparsity problems, and achieve higher feature information extraction performance than that of the traditional method. The DECOR model can also represent the interior features of attribute information structures in a relatively complex form. A user behavior extraction (UBE) module captures user behavior features, and a course attribute extraction (CAE) module captures course attribute feature information. A preference information regression (PIR) module integrates user behavior features and course attribute features into the obtained interaction features. We also conduct several experiments to evaluate the DECOR model's performance compared to that of the traditional recommendation approach using real-world datasets from the Korean education domain. The experimental results indicate that the proposed DECOR model provides better recommendation performance than existing methods. The main contributions of this study are as follows:  
* The DECOR model proposed in this study effectively learns the interaction features between user behaviors and course attributes to solve the information overload and data sparsity problems. * This study proposes a principled approach to integrating additional user and course feature information into the interaction features, thereby fully utilizing the combined information obtained from the user and course interaction features. The UBE module,CAE module, and PIR module components are very flexible and modular, so they can be effectively adapted to the DECOR model. * The study conducts several experiments utilizing real-world education datasets to demonstrate the DECOR model's effectiveness compared with that of the traditional recommendation approach. Additionally, we explore the proposed methodology's significance in the online education domain with a real-world dataset. The remainder of this study is organized as follows: Section 2 discusses related work on traditional recommender systems, deep learning techniques in recommender systems, and course recommender systems. Section 3 discusses the framework of the DECOR model and its components in detail. Section 4 describes the utilized experimental datasets and parameter settings and discusses the results of this study in detail. Section 5 summarizes the study, describes its limitations, and presents ideas for future work.