The evaluation showed users’ acceptance of extracted information about their educational interests, automatically generated from social network and enriched to find implicit interests. The proposal evaluation was made from the development of a prototype, three proofs of concept and a case study. It takes advantage of user’s spent time at the web. The recommendation approach is based on learning objects repositories, linked data and video repositories. The proposal is supported by Information Extraction Techniques and Semantic Web technologies for extraction, enrichment and definition of user’s profile and interests. This paper presents an infrastructure able to extract users’ profile and educational context, from the Facebook social network and recommend educational resources. The constant and ever-increasing use of social networks allows the identification of different information about profile, interests, preferences, style and behavior from the spontaneous interaction. In order to generate personalized recommendations, it is important to identify information that will help to define user profile and assist in identifying his/her interests. Recommendation systems may help users in this task. The proper choice of these educational resources is a challenge. There are several educational resources distributed in different repositories that address to a wide range of subjects and different educational goals.

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