# A Deep Learning-Based Course Recommender System for Sustainable Development in Education
## 5 Discussion and Conclusions  
This study aims to solve the issue of information overload in the online education domain. Choosing a personalized course on an online education website may be incredibly tedious and complicated. Therefore, it is a real challenge to help users make the correct choices from among the many courses available that meet their requirements. Traditional methods based on data mining such as CF try to model the user's preferences from user historical behavior such as clicking, rating, and viewing [9,66]. For example, CF only uses user, course, and enrolment information to model the behavior pattern. It is almost impossible to input comprehensive knowledge about a given course and does not address the recommendation process's user requirements. Furthermore, when historical behavior records are sparse, the existing approach will result a worse performance. This study proposed a novel DECOR model that can analyze the relationship between courses reasonably and efficiently, using all available information about the course (e.g., job opportunities, user interests, and so on). The experimental results indicate that the proposed DECOR model has better recommendation performance than traditional models, such as using only user, course, and preference. Although the amount of data we tested is not large, we believe that if we experiment with big data, better results will be obtained. This is because, according to the existing research results, when the data volume is very large, the deep learning technique produces better results than data mining. This study focuses on the following three main aspects in the field of study of course recommender systems:  
* Build a novel recommender system framework that can mitigate the information overload problem and restrict the number of user options to fewer alternatives. * Propose a new approach for the extraction and integration of data from multiple sources, thereby mapping relevant information regarding a course to obtain comprehensive knowledge about the recommended items. * Design and implement a deep learning-based recommender system that can analyze the relationship between courses reasonably and efficiently, using all available information about the course. * Add significant contributions to overcoming user cold start challenges on traditional approaches. Also, such an approach integrating multiple attributes and applying deep learning techniques can improve the ability to find courses for the target user. This study's main contribution is that it offers a personalized course recommender system that mitigates the information overload problem by building a comprehensive framework, which supports the extraction and integration of data from multiple features. The proposed framework also utilizes deep learning techniques to enhance the recommendation performance of the system and solve the cold-start problem caused by new users. Figure 5: This figure shows the impact of a negative sampling ratio on performance: (a) HR@10 performance based on the number of negative sampling size; (b) _NDCG@10_ performance based on the number of negative sampling size. The contribution of this study in terms of the expansion of the recommender system and the development of the e-learning industry is summarized as follows. * This study contributes to the knowledge of existing recommender systems by addressing how to solve problems in general and why they still have shortcomings. From a scientific aspect, it makes relevant contributions in the emerging domain of deep learning-based recommender systems. * A novel recommendation framework is proposed based on a deep learning technique containing user behavior information and course attribute information. The framework includes multiple feature integration and personalized course recommendations, and it provides suitable recommendations for user's individual requirements.