This paper improved the collaborative filtering recommendation algorithm by considering the nearest neighbor and directed similarity in Douban network data. Then, the improved algorithms were used to recommend books, movies and music for Douban users. The recommended results are carefully compared and analyzed in terms of three well-know indicators including accuracy, diversity and novelty. It is shown that the nearest neighbor algorithm has much lower computational complexity and the directed similarity algorithm obtains higher accuracy, while all these three algorithms have similar diversity and novelty of the recommended results, by comparing with the traditional collaborative filtering recommendation algorithm.