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.

Multi-Criteria Recommender Systems (MCRSs) improve personalization by incorporating multiple user preferences. However, their application in Technology-Enhanced Learning (TEL) remains limited due to challenges such as data sparsity, over-specialization, and cold-start problems. Traditional techniques, such as Singular Value Decomposition (SVD) and SVD + + , struggle to effectively model the complex interactions within multi-criteria rating data, leading to suboptimal recommendations. This paper introduces a hybrid DeepFM-SVD + + model, which integrates deep learning and factorization-based techniques to improve multi-criteria recommendations. The model captures both low-order feature interactions using factorization machines and high-order dependencies through deep neural networks, enabling more adaptive recommendations. To evaluate its performance, the model is tested on two multi-criteria datasets: ITM-Rec (TEL domain) and Yahoo Movies (non-TEL domain). The experimental results show that DeepFM-SVD + + consistently outperforms the traditional techniques across multiple evaluation metrics. The findings highlight significant improvements in accuracy, demonstrating the model’s effectiveness in sparse datasets and its generalization across domains. By addressing the limitations of existing MCRS techniques, this study contributes to advancing personalized learning recommendations in TEL and expands the applicability of deep learning-based MCRS beyond educational contexts.