Neighborhood-aware web service quality prediction using deep learning

Abstract

With the rapid growth of web services on the Internet, it becomes more difficult for users who want to choose the high-quality web services from a large number of functionally equivalent candidate services. Therefore, the prediction of quality of service (QoS) values according to the history of web services has received extensive attention. In recent years, deep learning has achieved great success in speech recognition, image processing, and natural language understanding. However, it is rarely applied to the service recommendation field. Therefore, a novel approach for QoS prediction named NDL (neighborhood-aware deep learning) is proposed. NDL first gets the Top-k neighbors of the user and the service through the Pearson correlation coefficient according to the service QoS information. Then, it extracts the potential features of the user neighbor and the service neighbor; after that, it inputs the QoS values of the user and the user neighbor as well as the QoS values of the service and service neighbors as a convolutional neural network. The results of experiments conducted on a real-world dataset demonstrate that the NDL significantly outperforms the current QoS prediction method in prediction accuracy.

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

“…Jin et al [8] proposed a deep learning model for predicting QoS of Web service, which builds the model through multi-layer perceptron (MLP) and convolution neural networks (CNN). Paradarami et al [19] also presented a deep learning framework to recommend new products to users.…”

“…Yin et al use the autoencoder improved by the substitution strategy to obtain nonlinear latent features of users and services, and missing QoS are generated by the traditional MF methods [45]. Since autoencoder generally uses one hidden-layer neural network to learn the embedding feature, Zhang et al adopt the MLP to model the nonlinear characteristics of embedding features [47], they also embed similar neighborhoods in MLP to further improve prediction accuracy [8]. Wu et al proposed a deep neural network for making QoS prediction with contextual information [38],…”

Contexts Enhance Accuracy: On Modeling Context Aware Deep Factorization Machine for Web API QoS Prediction

“…Yin et al uses the auto-encoder improved by the substitution strategy to obtain nonlinear latent feature of users and services, and missing QoS is generated by the traditional MF methods [31]. Since auto-encoder generally uses one hidden-layer neural network to learn the embedding feature, Zhang et al adopt the MLP to model the nonlinear characteristics of embedding features [32], they also embeds similar neighborhood in MLP to further improve prediction accuracy [33]. Wu et al proposed a deep neural network for making QoS prediction with contextual information [34], where a deep neural network is added to the end of FM in series for prediction.…”

QoS Prediction of Web Services Based on Reputation-Aware Network Embedding

“…Zhu et al [29] proposed a new context-aware reliability prediction approach, which solves the problem of data sparsity by constructing context-aware reliability models. Jin et al [19] proposed a novel deep learning model for QoS prediction, which uses the CNN to retrieve the potential nonlinear feature relationships and achieve accurate predictions.…”

“…Recently, researchers have proposed many state-of-the-art methods by utilizing representation learning [17], random walk [18] or deep learning [19] to alleviate the sparsity problem and retrieve appropriate neighborhood. Considering the complexity of service invocations, [20] developed a general context-aware matrix-factorization approach, which can make full use of implicit and explicit contextual information.…”