# State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning
###### Abstract  
Recommender systems (RSs) are increasingly recognized as intelligent software for predicting users' opinions on specific items. Various RSs have been developed in different domains, such as e-commerce, e-government, e-resource services, e-business, e-library, e-tourism, and e-learning, to make excellent user recommendations. In e-learning technology, RSs are designed to support and improve the learning practices of a student or an organization. This survey aims to examine the different works of literature on RSs that corroborate e-learning and classify and provide statistics of the reviewed articles based on their recommendation goals, recommendation techniques used, the target user, and the application platforms. The survey makes a prominent contribution to the e-learning RSs field by providing an overview of current research and traditional and nontraditional recommendation techniques to provide different recommendations for future e-learning. One of the most significant findings to emerge from this survey is that a substantial number of works followed either deep learning or context-aware recommendation techniques, which are considered more efficient than any traditional methods. Finally, we provided comprehensive observations from the quantitative assessment of publications, which can guide and support researchers in understanding the current development for potential future trends and the direction of deep learning-based RSs in e-learning. recommender systems; similarity metrics; recommendation goal; learning object; recommendation techniques; deep learning +
Footnote †: journal: Medical  
resources among people or communities. It uses different users' and items' characteristics, such as their interests, educational backgrounds, levels of expertise, geographical locations, and so on, to propose sequences or novel resources that might interest users [5]. However, when providing recommendations for e-learning contexts, the system should consider the users' interests and learning goals [6]. The past seven years have seen increasingly rapid advances in RSs. One of the primary concerns of the RSs research community within that period was the concept of choosing the candidate recommendation technique that can be used to compute users' preferences on new items efficiently. Many of today's RSs for learning are built based on the three primary traditional recommendation techniques, the same as RSs in other domains, such as e-commerce, namely collaborative filtering, content-based filtering, and hybrid techniques [7]. However, along with this growth of RSs, there is increasing concern over several significant drawbacks related to recommendation techniques. Over-specialization, cold-start, and data sparsity problems are the major drawbacks of traditional methods [8]. Nontraditional methods such as deep learning, context-aware, and multi-criteria recommendations extending traditional techniques have recently emerged. The existence of many alternatives in choosing the technique to use helped tremendously to improve the accuracy of recommendations that can be offered to users. Presently, RSs go beyond using only traditional recommendation techniques by considering the pedagogical requirements of the user instead of only the preferences. In this survey, we present, among other things, a synthesis of prior work on RSs, and an overview of both traditional and other techniques of building RSs for learning that are less problematic and more efficient than traditional recommendation techniques. Furthermore, another critical aspect of building RSs is generally related to various user tasks that the system can support in a particular application domain. In technology-enhanced learning, RSs help learners achieve their desired learning goal by recommending a sequence of items, finding novel items, providing annotation in context, finding learning peers, recommending learning pathways, etc. [7].