# A Deep Learning-Based Course Recommender System for Sustainable Development in Education
## 5 Discussion and Conclusions
In summary, we developed deep learning-based hybrid recommendation algorithms designed to extract and integrate information from multiple features to improve recommendation performance by effectively solving the information overload and data sparsity problems. * The proposed deep learning-based recommendation model is designed to extract and integrate information from multiple sources, enhancing recommendation quality and overcoming heterogeneity of course information. It can also be applied in diverse recommender system domains that change with the user's interests and attribute features. * We develop a personalized recommender system, evaluate its effectiveness, and compare it with state-of-the-art methods on real-world datasets. The proposed approach can be applied to other online education platforms such as K-MOOC, edX and Coursera for all users. The developed deep learning-based recommendation approach for the online education website domain improves recommendation performance. There are, however, still certain limitations encountered when applying it to online education websites. First, building deep learning-based recommendation algorithms is a challenging and complicated process that requires knowledge engineering skills. Additionally, deep learning knowledge in the online education domain requires expertise in this area. Second, course recommender system studies in online education domains have been a major challenge for many researchers. This is due to the lack of publicly available standard datasets for evaluating recommender systems. Other domains, such as movies and books, have many public datasets that can be used for evaluation purposes. However, there are not enough public datasets for training domain recommender systems. Hence, the assessment of a course recommender system is challenging. Third, this study mainly reported results on technical aspects related to course recommendation algorithms. In other words, we not conducted behavioral studies when real users applied the algorithm proposed in this study. Future studies will improve the ability of the DECOR model to address the high-dimensionality and sparsity problems in practical online education platforms and will study more effective data reduction algorithms. Several experiments were conducted with a deep learning-based recommender system, and its performance was evaluated compared to that of other traditional recommender systems. Experimental results show that the utilized deep learning approach performs better than the conventional methods. Therefore, further study is needed to apply various other deep learning algorithms, such as a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). Furthermore, investigating how user behavior features and course attribute features influence course recommendation algorithms' scalability and reliability will be another research area. Other feature interactions need to be considered in our future studies to develop highly personalized course recommendation algorithms. In addition, we will apply the proposed algorithm to the actual website for demonstration analysis. Through this work, we will effectively analyze the features that satisfy the actual user step by step. **Author Contributions:** Conceptualization, J.K. and Q.L. ; methodology, Q.L. ; data curation, Q.L. and J.K.; writing--original draft preparation, J.K. and Q.L. ; writing--review and editing, J.K.; supervision, J.K. Both authors have read and agreed to the published version of the manuscript. This research was supported by the BK21 FOUR Program (5199990913932) was funded by the Ministry of Education (MOE, Korea) and the National Research Foundation of Korea (NRF). **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.