Qos prediction for service selection and recommendation with a deep latent features autoencoder

Abstract

The number of services on the Internet has increased rapidly in recent years. This makes it increasingly difficult for users to find the right services from a large number of the functionally equivalent candidate. In many cases, the number of services invoked by a user is quite limited, resulting in a large number of miss ing QoS values and sparseness of data. Consequently, predicting QoS values of the services is important for users to find the exact service among many functionally similar services. However, improving the accuracy of QoS prediction is still a prob lem. Despite the successful results of the proposed QoS prediction methods, there are still a set of issues that should be addressed, such as Sparsity and Overfitting. T address these issues and improve prediction accuracy. In this paper, we propose a novel framework for predicting QoS values and reduce prediction error. This frame work named auto-encoder for neighbor features (Auto-NF) consists of three steps. In the first step, we propose an extended similarity computation method based on Euclidean distance to compute the similarity between users and find similar neigh bors. In the second step, we form clusters of similar neighbors and partition the initial matrix into sub-matrices based on these clusters to reduce the data sparsity problem. In the third step, we propose a simple neural network autoencoder that can learn deep features and select an ideal number of latent factors to reduce the over fitting phenomenon. To validate and evaluate our method, we conduct a series of experiments use a real QoS dataset with different data densities. The experimental results demonstrate that our method achieves higher prediction accuracy compared to existing methods.