Collaborative filtering (CF) is the core of most of today's recommender systems. Conventional CF models focus on the accuracy of predicted ratings, while the actual output of recommender systems is a list of ranked items. In response to this problem, this research introduces technologies in the field of learning to rank into recommendation algorithms and proposes a listed collaborative ranking algorithm based on the assumption that the rating matrix is locally low-rank. It directly uses list-wise ranking loss function to optimize the matrix factorization model. Significant improvement on operation speed is achieved and verified by experiment. Experiments on three real-world recommender system datasets show that the proposed algorithm is a viable approach compared with existing recommendation algorithms.