Improved FunkSVD Algorithm Based on RMSProp

Abstract

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.

Surface Crack Detection of Steel Structures in Railroad Industry Based on Multi-Model Training Comparison Technique

“…The use of the AdaDelta optimizer allows for fast descent gradients and complete regression [21]. The AdaDelta algorithm does not have a learning rate as a hyperparameter, it replaces the learning rate in the RMSProp algorithm by using a term about the exponentially weighted moving average of the squared updates of the independent variables [22,23]. This makes it possible to extract image features efficiently in a short period of time.…”