# State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning
## 5 Conclusions and Future Work  
RSs for learning has become an independent and vital research domain aimed at designing, implementing, testing, and evaluating systems to know how efficient recommendation algorithms are or investigate t whole systems' performances. Other evaluation methods include assessing the impact of systems toward achieving desired learning goals and measuring users' satisfaction and trust in systems. Different systems have been developed to run on various computer devices and environments. This research was undertaken to give an overview of various RS techniques and statistics of research papers on RSs for learning based on (1) the technique(s) used to design and implement the system; (2) the users' tasks supported by the systems; (3) the platforms (chatbot, mobile or web-based); and (4) the kind of users of the system (students or teachers). This survey has shown significant findings regarding the recommendation goals, techniques, and platforms used for its implementation. Firstly, it was observed that a great deal of research was implemented using traditional techniques, mostly collaborative filtering and hybrid approaches. Still, a substantial number of works also followed either deep learning, hybrid, or context-aware recommendation techniques, which are considered more efficient than traditional methods. Secondly, in terms of supporting user tasks, it was shown that most systems either recommended \"sequence\" or \"find all good learning objects\" to the learner. The third significant finding concerned the platforms for which the proposed systems were designed to run, showing that very few systems were designed to run on mobile platforms. This result has shown a great need to improve and change our research direction toward mobile-based applications, as much of the current literature has confirmed that smartphones and tablets reduce the frequency of using ordinary cell phones and, to a large extent, replace personal computers and other devices. One of the most striking new recommendation techniques that can significantly improve recommendation accuracy is the multi-criteria recommendation technique, which has been used and proven by researchers in industries and academic institutions to provide more accurate predictions than traditional techniques [152]. Many researchers have also established its efficiency in the RS domain [27]. Few recent research publications, such as [48; 72; 84; 91; 120], have applied the multi-criteria recommendation technique. Nevertheless, another emerging area that goes in line with RSs for learning in terms of faster growth and development is the domain of social networking, which, if integrated with RSs for learning, can enable learners to connect, communicate, and share knowledge online. Fulfilling this can make social media and RSs major players in e-learning, as many learners can get in touch with each other in various ways. Unfortunately, even though it is not clearly stated within the aim of this survey, we tried to filter those systems developed to take advantage of social media to improve learning object recommendation. None of the papers were purposely designed as social network-based RSs for learning. The implication is that there is a need for research in this domain to focus on merging different social network sites with RSs for effective learning object recommendations. Generally, some gaps were identified in this survey. Due to these gaps, we suggested that future work should focus on the following:  
* Building RSs for learning to support various user tasks to provide serendipito-us recommendations;
* Putting more effort into developing mobile-based and chatbot-based RSs for learning;
* Building RSs specifically for teachers, as only 6% of our reviewed papers were designed to be used by teachers;
* Nontractional RSs, especially multi-criteria ratings RSs, which have direct applications in this domain, are also promising approaches for future work.