# Smart System for the Retrieval of Digital Educational Content
## 2 Related Work
### State of the Art
The most similar users are extracted through nearest neighbor algorithms. These correlation-based algorithms are used to calculate an index score on the usefulness of LOs through the analysis of comments from students with similar profiles. This algorithm helps prevent the cold start problem when no data are available and there are no values for the LO. However, all of the selected LOs are treated equally and indistinguishably, making it possible to assess user preferences more precisely, affecting the very pattern of user preferences. The use of algorithms based on biological models, such as ACO (Ant Colony Optimization) is the basis of [PERSON] [52], which proposes a set of attributes based on a colony of ants to help students find their way through an adaptive LO model more efficiently. This work is interesting because it bases its recommendation on the generation of different learning paths through different sets of LOs. The ultimate goal of these solutions is to help the user learn about a specific topic by recommending a sequence of LOs that creates an optimal learning path. Older studies evidenced the lack of operational solutions [22; 39; 47], while more recent works [52; 53; 54; 55; 56; 57] point to the need for improving the search mechanism for the selection of LOs. There is a growing number of papers that propose systems for the recommendation of learning resources. Some of them take into account the educational content described by metadata and the user's learning context as the basis of recommendations. They propose a schema called Contextualized Attention Metadata (CAM) for the capture of information on the different processes that occurred during the LO life cycle, including their creation, labeling, supply, selection, use, and maintenance. These studies propose metrics for use in the LOM standard and the CAM schema for the recommendation of the retrieved LOs and their classification according to criteria such as popularity ranking, the similarity of items, the number of downloads, and more. A highly interesting field of study is understanding how these rankings contribute to the selection of LOs and how they combine with each other, questions that have not been answered yet. All the reviewed state-of-the-art proposals concluded that effective recommendation mechanisms can be created by incorporating mechanisms for the assessment of the characteristics of educational content (LOs in our work), user context and the user's interaction with the content. However, taking a closer look at the reviewed LO search proposals, we have noticed that they lack applications in the real environment. In short, there is no standard that would cover all the needs of the process of searching, retrieving and reusing learning objects. Current technology does not give solutions to the automatic or semi-automatic search and retrieval of LOs and, therefore, the reuse of learning resources. For this reason, this paper proposes a flexible and modular architecture, allowing for the intelligent retrieval of the educational digital content stored in LO repositories. The architecture enables the interoperability of layers. It would also be important to incorporate efficient management techniques that would be capable of dealing with future challenges for the efficient management of services and platforms.