# Smart System for the Retrieval of Digital Educational Content
## 2 Related Work
### State of the Art  
To standardize the use of LOs it is necessary to facilitate their search and retrieval [30]. Several studies have already pursued the goal of improving the visibility of LOs. BILDU [31] has proposedan architecture that consisted of a set of horizontally integrated vertical search engines. The BILDU architecture has a single interface, which supports the incorporation of social recommendation mechanisms. Content integration and repository searches through peer-to-peer mechanisms are proposed in Edutella [32]. Edutella's architecture is based on mediators which enable the integration of LORs [33]. Different methods are applied to retrieve educational content [34], including the metric proposed for the calculation of similarity between different LOs. Content retrieval processes will only be considered successful if they enable the consensual use of educational content and its structures. It is crucial that the research community focus its current efforts on establishing the characteristics, constraints or conditions for the appropriate use of these learning resources. This process must, therefore, involve several disciplines, a study of the semantic aspects of resources, an improvement of the communication standards and the management of large databases. The Ontology Query Expansion Algorithm [35] adapts an ontological approach to LO retrieval by expanding the query algorithm with semantic aggregation and a dynamic mechanism for searching and filtering LOs whose theme is genetics. This mechanism has been validated in the MERLOT (Multimedia Educational Resource for Learning and Online Teaching) repository where the mechanism searched for LOs using Gene Ontology [36]. The mechanism is connected to the MERLOT Restful service for the retrieval of contents. Many varied solutions have been proposed [37; 38; 39; 40], although most of them have not been implemented in real environments, and the majority do not focus specifically on using the LO paradigm for the retrieval of educational content. Search results have been improved by proposals of more complex solutions that implement different technologies and social recommendations. Only two proposals, [PERSON] et al. [41] and [PERSON] et al. [42], make use of agent technology, but neither of the two approaches coordinates the completion of tasks by agents within the system. Our review of the state of the art confirms that it is necessary to continue working for greater interoperability and efficiency in the reuse of resources. It has also been established that to achieve interoperability and efficiency, it is important to develop features such as adaptability, flexibility, and accessibility. Due to the vast number of LORs, a major challenge with regard to LO recommendation is to find the LOs that best suit the users' search criteria. E-learning researchers and developers have begun to integrate information retrieval techniques with recommendation technologies [43; 44], collaborative filtering [45], web mining [46]. The information extracted by information filtering techniques is associated with the attributes of the educational resource (content-based approach) and the user context (collaborative approach). One of the first works developed in this context was Altered Vista: a system in which teaching techniques are evaluated by a recommendation engine with collaborative filtering [47]. This work researches ways of collecting user reviews of learning resources and of propagating them in the form of word-of-mouth recommendations. RACOFI is a Rule-Applying Collaborative Filtering system whose architecture enables custom educational content selection [48; 49], it improved collaborative filtering in its proposal of the ecological approach to designing e-learning systems. The key aspects of this proposal are that it considers the gradual accumulation of information and that it focuses on end users. Some solutions take a hybrid approach, such as [PERSON] [50] and [PERSON] et Abdullah [51], which make use of algorithms based on the user reviews of learning objects.