An embedding based factorization machine approach for web service QoS prediction

Abstract

As an important property of Web services, Quality of Service (QoS) is usually engaged for describing the non-functional characteristics of Web services. However, QoS value is considerable sparse since users only invoke a limited number of services in the real-world applications. In this way, predicting QoS value is a good choice to solve such ‘sparsity’ problem. Although several methods have been proposed to predict QoS value for users, most of them are always time-consuming and expensive to implement. To solve the drawbacks of high dimensionality and huge sparse, we introduce embedding technique to map data from resource space to target space in injective and structural-preserving way. To efficiently express pairwise interactions in sparse datasets, we further introduce factorization machine, which is an impactful algorithm to deal with sparse data prediction in the world of machine learning and can be computed in linear time. Based on the above characteristics of our scenario and the advantages of factorization machine and embedding, this paper proposes an embedding based factorization machine approach to predict missing QoS values for Web services. First of all, user id and service id are encoded by one-hot encoding. And then, the one-hot encoding of user id and service id are mapped to different embedding vectors. Finally, the embedding vectors are regarded as implicit vectors and the idea of factorization machine is exploited to make missing QoS value prediction. Experiments on real-world dataset validate the effectiveness of our approach, which outperforms the other state-of-the-art methods in terms of QoS prediction accuracy.