To solve the problem of low accuracy in the traditional FunkSVD recommendation algorithm, an improved FunkSVD algorithm (RM-FS) is proposed. RM-FS is an improvement of the traditional FunkSVD algorithm, using RMSProp, a deep learning optimization algorithm. The RM-FS algorithm can not only solve the problem of reduced accuracy of the traditional FunkSVD algorithm because of iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, achieving the effect of improving the accuracy of the traditional algorithm. The experimental results show that the RM-FS algorithm proposed in this paper effectively improves the accuracy of the recommendation algorithm, which is better than the traditional FunkSVD recommendation algorithm and other improved FunkSVD algorithms.

Nowadays, the increasing amount of network data information and the development of big data technology have brought development opportunities and challenges to the recommendation system. The model-based collaborative filtering algorithm has become one of the mainstream algorithms in the recommendation system. For example, the Funk Singular Value Decomposition (FunkSVD) algorithm. However, in the face of big data calculations, data sparseness and iterative oscillations often affect the accuracy of the FunkSVD algorithm. Moreover, when the data volume is in units of GB or more, the FunkSVD algorithm runs slowly and is not effective. Therefore, we propose an improved FunkSVD algorithm (IFABG) based on RMSProp (Root Mean Square Prop) and GPU (Graphics Processing Unit) to solve this problem. Firstly, we use RMSProp algorithm to improve the traditional FunkSVD algorithm, alleviate data sparseness and iterative shock, and improve the prediction accuracy of the algorithm. Next, we implemented the parallelization of the improved FunkSVD algorithm in the GPU, which increased the calculation speed of the algorithm. Finally, we verify the IFABG algorithm under the Movielens dataset. The experimental results show that the IFABG algorithm is very suitable for processing sparse data, and it alleviates the iterative shock, and the prediction accuracy rate is about 30% higher than that of the traditional FunkSVD algorithm. The experimental results also show that the IFABG algorithm has a good acceleration effect. Under the same size data set, the IFABG algorithm is faster than the traditional FunkSVD, and the acceleration ratio can be as high as 19.27.