Several of the existing major social networking services such as Facebook and Twitter, recommend friends to their users based on social graphs analysis, or using simple friend recommendation algorithms such as similarity, popularity, or the “friend's friends are friends,” concept. However these approaches, even though intuitive and quick, they consider few of the characteristics of the social networks, while they are typically not the most appropriate ways to reflect a user’s preferences on friend selection in real life. To overcome these problems in this paper a novel scheme is proposed for recommending friends in social media, based on the analysis and vector mapping of online lifestyles. In particular for each user a vector is created that captures her/his online behavior. Then, in the simple case, vector matching is performed so that the top matches are selected as potential friends. In a more sophisticated case, the most similar profiles to the user under investigation are detected and a collaborative recommendations approach is proposed. Experimental results on real life data exhibit the promising performance of the proposed scheme.