Online social networking systems are rapidly becoming popular for users to share, organize and locate interesting content. However, these systems have increasingly been employed as platforms to spread spam and irrelevant content, abusing valuable human attention and service resource. In this paper, we propose a social reputation model to guide users to browse desirable content. First, we compute the statistical correlation between different users to distinguish various user interests; then, since a user's friends are usually trustworthy and share similar interest, we further exploit the inherent friend relationships to perform reliable social enhancements of vote history extension and efficient reputation estimation. Our social reputation model provides strong incentives for user cooperation, and moreover, our model can handle practical problems of inactive users, unpopular content and Sybil attacks effectively and efficiently. Our evaluation on a large-scale network validates our analysis, and shows that our social reputation model can help users find the desirable content in various scenarios with a precision of 94%.