In the era of big data, personalized recommendation system is an effective means of information filtering. One of the main factors that affect the prediction accuracy is data sparsity. Slope One online rating prediction algorithm uses simple linear regression model to solve data sparisity problem, which is easy to implement and has quick score rating, but its training stage needs to be offline because of high time and space consumption when generating differences between items. To solve above problems, a simplified Slope One algorithm was proposed, which simplified the most time-consuming procedure in Slope One algorithm when generating items' rating difference in the training stage by using each item's historical average rating to get the rating difference. The simplified algorithm reduces the time and space complexity of the algorithm, which can effectively improve the utilization rate of the rating data and has better adaptability to sparse data. In the experiments, rating records in Movielens data set were ordered by timestamps then divided into the training set and test set. The experimental results show that the accuracy of the proposed simplified Slope One algorithm is closely approximated to the original Slope One algorithm, but the time and space complexity are lower than that of Slope One, it means that the simplified Slope One algorithm is more suitable for large-scale recommendation system applications with rapid growth of data.