With the development of the IT industry, the information resources and services that people come into contact with have been growing. The sharing and interaction of these two types of information has led to further growth in data resulting in the advent of the era of big data. However, due to the complexity of information, such as low utilization, the confusion, and disorder, it will be a great challenge for service providers to offer customers services with high quality and accurate quality. The recommendation system is a solution adopted by e-marketers for such problems. This paper proposes a SPARK-based recommendation algorithm called SARF. The SARF first reads the original data from the HDFS and uses Spark to yield the datasets. Then, by using these datasets, the optimal recommended model is generated through iterative process. Finally, we gain the results which are recommended to the user by the optimal model. The final experimental results show that our SARF framework can effectively reduce the recommendation error and solve the problem of cold start.