In order to solve the bottleneck problems of processing speed and resource allocation, a Spark based matrix factorization recommendation algorithm was proposed. Firstly, user factor matrix and item factor matrix were initialized according to historical data. Secondly, factor matrix was iteratively updated and the result was stored in memory as the input of next iteration. Finally, recommendation model was generated when iteration ended. The experiment on MovieLens shows that the speedup is linear and the proposed Spark based algorithm can save time and significantly improve the execution efficiency of collaborative filtering recommendation algorithm.