Nahia Ugarte‡, Mikel Larrañaga‡, Ana Arruarte‡Corresponding author: Mikel Larrañaga ( mikel.larranaga@ehu.eus ) © Nahia Ugarte, Mikel Larrañaga, Ana Arruarte. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use. Citation:
Ugarte N, Larrañaga M, Arruarte A (2022) The Use of Recommender Systems in Formal Learning. A Systematic Literature Mapping. JUCS - Journal of Universal Computer Science 28(4): 414-442. https://doi.org/10.3897/jucs.69711 |  |
Recommender Systems provide users with content or products they are interested in. The main purpose of Recommender Systems is to find, among the vast amount of information that is available or advertised on the Internet, content that meets the user’s needs i.e., a product or content that satisfies his or her wishes. These systems are being used more and more in many of the services of our daily lives. In this paper, a systematic mapping review that explores the use of Rec- ommender Systems in formal learning stages is presented. The paper analyzes what kinds of items the Recommender Systems suggest, who the users that receive the recommendations are, what kinds of information the Recommender Systems use to carry out the recommendation process, the algorithms and techniques the Recommender Systems employ and, finally, how the Recommender Systems have been evaluated. The results obtained in the review will make it possible to iden- tify not only the current situation in this field but also some of the challenges that are still to be faced.