We propose a max margin based semi-supervised reranking method for multimedia information retrieval. We use hypergraph regularization to preserve the neighborhood of the sample in the original space and introduce the labeled and unlabeled sample information to construct the objective function, so as to achieve full and efficient use of data information for ranking. By using a small amount of annotation samples to construct the priority relationship pairs, the priority information between samples is introduced into the objective function to construct a ranking learning model. This method can show users in priority the results that meet their demand better, and improve the retrieval accuracy. The experimental results on MSRA-MM 1.0 dataset suggest the proposed method provides superior performance compared with several state-of-the-art methods.