The web has largely become a very social environment and will continue to become even more so. People are not only enjoying their social visibility on the Web but also increasingly participating in various social activities delivered through the Web. In this paper, we propose to explore a user's public social activities, such as blog-ging and social bookmarking, to personalize Internet services. We believe that public social data provides a more acceptable way to derive user interests than more private data such as search histories and desktop data. We propose a framework that learns about users' preferences from their activities on a variety of online social systems. As an example, we illustrate how to apply the user interests derived by our system to personalize search results. Furthermore, our system is adaptive; it observes users' choices on search results and automatically adjusts the weights of different social systems during the information integration process, so as to refine its interest profile for each user. We have implemented our approach and performed experiments on real-world data collected from three large-scale online social systems. Over two hundred users from worldwide who are active on the three social systems have been tested. Our experimental results demonstrate the effectiveness of our personalized search approach. Our results also show that integrating information from multiple social systems usually leads to better personalized results than relying on the information from a single social system, and our adaptive approach further improves the performance of the personalization solution. © 2010 ACM.