# State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning
## 4 Results and Discussion
### Deep Learning-Based RS in E-Learning Research  
Deep learning-based RS has been implemented in e-learning mainly because of its flexibility, representation learning, sequence modeling, and nonlinear transformation. This \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Country** & **Alpha Code** & **No. of Papers** & **Country** & **Alpha Code** & **No. of Papers** & **Country** & **Alpha Code** & **No. of Papers** \\ \hline China & CN & 34 & Pakistan & PK & 2 & Mexico & MX & 1 \\ \hline India & IN & 12 & Ecuador & EC & 2 & Colombia & CO & 1 \\ \hline USA & US & 10 & Germany & DE & 2 & Canada & CA & 1 \\ \hline Morocco & MA & 9 & Bangladesh & BD & 2 & Israel & IL & 1 \\ \hline Indonesia & ID & 4 & South Korea & KR & 2 & Czech & CZ & 1 \\ \hline Brazil & BR & 4 & Serbia & RS & 1 & Australia & AU & 1 \\ \hline Greece & GR & 3 & Saudi Arabia & SA & 1 & Thailand & TH & 1 \\ \hline Jordan & JO & 3 & Malaysia & MY & 1 & Taiwan & TW & 1 \\ \hline Vietnam & VN & 2 & Italy & IT & 1 & New Zealand & NZ & 1 \\ \hline Tunisia & TN & 2 & Philippines & PH & 1 & France & FR & 1 \\ \hline Spain & ES & 2 & Nigeria & NG & 1 & Lithuania & LT & 1 \\ \hline Netherlands & NL & 2 & Japan & JP & 1 & UK & GB & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Regional distribution of surveyed papers. Figure 7: Surveyed Paper type. section analyzes different works from the literature that have been proven to be effective and implemented using deep learning. Ref. [42] proposed a content-based learning resource recommendation algorithm based on CNN to solve the automatic multimedia learning resource recommendation problem. The proposed CNN is used to predict the latent factors from the text information of multimedia resources. To train the CNN, the authors solved the input and output using a language model, a proposed latent factor model that was regularized by the \(L_{1}\)-_norm_, and the split Bregman iteration method. Their proposed approach uses text information directly to make content-based recommendations without tagging. The authors believe that the Bregman iteration method greatly improves CNN training efficiency. Ref. [53] aimed to explore how to take advantage of the existing MOOCs to improve the learning quality of formal courses provided by accredited educational establishments. Therefore, the authors proposed MOOCs RSs for formal learning platforms. The platform was able to recommend effective MOOCs to learners in the formal curriculum. Their proposed system is based on Siamese long short-term memory (LSTM) networks to measure the semantic similarity between courses' descriptions. Ref. [54] proposed a deep learning-based course recommendation model that generates views from a different perspective and makes intelligent course recommendations for students. The proposed approach was implemented by collecting various data types to generate student and curriculum models. The data were then generated by studying the actual relationship between specific models. Finally, a deep learning technique was used to extract key features, select different recommended features, and generate different views to further recommend to students. Ref. [56] proposed a deep learning-based RS that simultaneously integrates content (heterogeneous) information and sequence data in the candidate-generation process. The proposed model utilizes the sequence data of user history and the item's heterogeneous information to improve the prediction accuracy of deep RSs. Their proposed approach modified the first layer of the deep learning-based recommendation model by concatenating a vector containing data regarding item order. Thus, the average preference vector and the sequential preference vector models can be aware of more information on the user, leading to a more accurate recommendation. Ref. [66] presents a novel model of full-path learning recommendation in an online learning environment. This model relies on clustering and machine learning techniques based on a feature similarity metric on learners.