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Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users.

Resumen: El uso de plataformas colaborativas en educación se ha incrementado en los últimos años. La gran cantidad de usuarios registrados en las mismas ha originado el problema de no saber con quién interactuar. Muchas veces la persona con quien se interactúa no es la idónea y resultan interacciones no exitosas. En este artículo, se presenta un sistema recomendado que sugiere personas idóneas para interactuar en una plataforma colaborativa. Palabras clave: Sistemas recomendadores, personalización, plataformas colaborativas, modelos de usuarios.Abstract: The use of collaborative platforms in education has increased in recent years. The large number of registered users originated the problem of not knowing who interact. Many times, the person with whom they interact is not ideal, resulting in not successful interactions. This article presents a system that recommends suitable people to interact in a collaborative platform.