Parallel Algorithm of Improved FunkSVD Based on GPU

The increasing amount of network data and the development of big data technology have brought development opportunities and challenges to recommendation systems. Model-based collaborative filtering has become a mainstream algorithm in recommendation systems. For example, the Funk singular- value decomposition (FunkSVD) algorithm. However, in 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 ineffective. Therefore, we propose an improved FunkSVD algorithm (IFABG) based on the root-mean-square prop (RMSProp) and graphics processing unit (GPU) to solve this problem. First, we used 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 parallelization of the improved FunkSVD algorithm in the GPU, which increased the calculation speed of the algorithm. Finally, we verify the IFABG algorithm using the Movielens dataset. The experimental results show that the IFABG algorithm is very suitable for processing sparse data, and it alleviates the iterative shock. The prediction accuracy rate is approximately 30% higher than that of the traditional FunkSVD algorithm. The experimental results also demonstrate that the IFABG algorithm has a good acceleration effect. For the same size dataset, the IFABG algorithm is faster than the traditional FunkSVD, and the acceleration ratio can be as high as 19.27.