# 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
This paper builds on previous research in [110] by improving the tractability of generating a recommendation from the architecture introduced in [110] and applying this improvement to the task of course preparation recommendation, as it was applied in the prior work, and extending its application to MOOC quiz preparation, where it has not yet been applied. The proposed improvement in tractability was achieved by treating the space of recommendations (e.g., courses or course pages) as weights in a neural network that stochastic gradient descent can learn on a per-learner basis to optimize for a future goal (e.g., grade on a course or quiz), while the prior work [110] approached this optimization by enumerating every possible recommendation and evaluating it as an input to the model. Ref. [125] proposed an attention-based CNN to obtain a user's profile, predict user ratings, and recommend the top n courses for MOOC learners. The proposed approach was implemented by first representing learner behaviors and learning histories into feature vectors. The attention mechanism was then used to train the neural network to improve relevance estimation according to the differences between the estimation scores and the actual scores given by users. Finally, the trained model recommended courses to learners. The authors presented a web-based course RS framework. Their proposed system can identify the learning habits of students and assist them in identifying preferred courses. However, it may still suffer the problem of recommending similar courses due to the huge number of courses with the development of MOOCs. Ref. [132] proposed a personalized course recommendation system based on eye-tracking technology and deep learning. The proposed approach is a novel click-through rate (CTR) model for personalized online course recommendations with discriminative user, item, and cross features. The feature representation ability of the CTR model was improved, and the serious challenge of cold-start problems was alleviated. Furthermore, transfer learning was introduced to deal with the problem of insufficient data in model training. More specially, eye-tracking technology was applied to learn the users' cognitive styles, which are visualized with a heat map and fixation point trajectory to achieve a reasonable interface for personalized course recommendations. Finally, the recommendation interface was sent to the learners according to the users' cognitive styles. Ref. [142] proposed an efficient e-learning recommendation (EELR) system for user preferences using a hybrid optimization algorithm (HOA). The EELR system constructs an HOA with a deep recurrent neural network (DRNN) and an improved whale optimization (IWO) algorithm. The proposed approach was implemented by first utilizing DRNN to order and analyze the e-learner types with their group, which was used to classify with deep multi-layers. After that, the learners' conduct and inclinations were examined by completing the mining of the arrangements observed frequently by the IWO calculation. Finally, the proposal of e-learning depends on the appraisals compared to these arrangements observed frequently. This proposed system was to implement and validate numerous e-learning entries against client inclinations over some undefined time frame and proved to be more proficient and exact in contrast with the customary recommender framework. The authors concluded that this strategy could help learners to grasp the knowledge system and learning direction and improve their learning efficiency.