A novel microblog recommendation method combining the tag correlation with the user social relation is proposed via analyzing microblog features and the deficiencies of existing microblog recommendation algorithm.Specifically

we establish a tag retrieval strategy to add tags for unlabeled users and users with few tags

and then build the user-tag matrix and obtain user-tag weights.In order to solve the problem of sparsity of the matrix

we investigate the correlation between the tags to update the user-tag matrix.Considering the significance of user social relation for microblog recommendation

a user-user social relation similarity matrix is constructed and a mechanism is designed to iteratively obtain user interest.Experimental results show that the algorithm is effective in microblog recommendation.