[EN]Recommender Systems (RSs) play a very important role in
web navigation, ensuring that the users easily find the information they are
looking for. Today’s social networks contain a large amount of information
and it is necessary that they employ mechanism that will guide users to
the information they are interested in. However, to be able to recommend
content according to user preferences, it is necessary to analyse their profiles
and determine their preferences. The present study presents the work related
to different recommender systems focused on two different hybrid models.
Both of them are using a Case-Based Reasoning (CBR) system combined with
the training of an Artificial Intelligence (AI) algorithm. First, some information
is analyzed and trained with an AI algorithm in order to determine
relevant patters hidden on the information. Then, the CBR system extends
the system using a series of metrics and similar past cases to decide whether
the recommendation is likely to be recommended to a user. Finally, the last
step on the CBR is to propose recommendations to the final user, whose job
is to validate or reject the proposal feeding the cases database
