Abstract: In the era of big data, personalized recommendation system is an effective means of information filtering. One of the main factors which affect the prediction accuracy is data sparsity. Slope One online rating prediction algorithm uses simple linear regression model to solve data sparisity problem. It is easy to implement and predicts rating speedily. The disadvantage is in the training stage, generating differences between items is more time-consuming. As the data in recommendation system grow rapidly, the training time increases accordingly. 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 reduced the time and space complexity of the algorithm. It 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 proposed simplified Slope One algorithm’s accuracy is closely approximated to the original Slope One algorithm. But the time and space complexity are lower than the original Slope One. These indicates simplified Slope One algorithm is more suitable for large-scale recommendation system applications with rapid growth of data.