Web service classification is one of the common approaches to discover and reuse services. Machine learning methods are widely used for web service classification. However, due to the limited high-quality services in the public dataset, the state-of-the-art deep learning methods can not achieve high accuracy. In this paper, we propose a transfer learning approach Tr-ServeNet to reuse the knowledge of the App classification problem for web service classification. We pre-train a deep learning model for the App classification problem, in which the dataset contains high-quality data from Apple Store, and then transfer the embedded and extracted features to assist web service classification. To demonstrate the effectiveness of our approach, we compare the proposed method with other existing machine learning methods on the 50-category benchmark with 10, 000 real-world web services. The experimental results indicate that the proposed transfer learning method can reach the highest Top-1 accuracy in the benchmark of service classification.

Automatic service classification plays an important role in service discovery, selection, and composition. Recently, machine learning has been widely used in service classification. Though promising results are obtained, previous methods are merely evaluated on web services datasets with small-scale data and relatively balanced data, which limit their real-world applications. In this paper, we address the long-tailed web services classification problem with more categories and imbalanced data. Due to the long-tailed distribution of datasets, the existing machine learning and deep learning methods cannot work well. To deal with the long-tailed problem, we propose a normalized multi-head classifier learning strategy, which effectively reduces the classifier bias and benefit the generalization capacity of the extracted features. Extensive experiments are conducted on a large-scale long-tailed web services dataset, and the results show that our model outperforms the 11 compared service classification methods to a large margin.