Interactions among people have substantially changed since the emergence of social networks, the expansion of the Internet and the proliferation of connected mobile devices, and so have the possibilities of collaborative learning, with the inclusion of new e-learning platforms. From this point, assessing human knowledge in these virtual environments is not a trivial task. This work presents a novel cloud-computing-based service which relies on advanced artificial intelligence mechanisms to infer knowledge and interest from users considering the aggregated data presented from/to these users in different social networks. This way it is possible to assess with a certain degree of confidence the user knowledge level in different topics as well as recommend additional specific education related to his/her former studies in order to get a better/desired job. The results show that the proposed method is effective. To study the impacts of the influencing factors, a sensitivity analysis has been carried out. It is utility to understand the role of different indicators on aggregated value.