Thanks to the rapid development of service-oriented computing (SOC) technologies, the number of Web services, such as Web API, is increasing rapidly. However, this brings some difficulties for mashup (a kind of Web API composition) developers to choose appropriate Web Services to build their projects. Finding required Web APIs from a large number of candidates and recommending them to developers has become a vital issue in mashup development. The traditional collaborative filtering algorithm has the problems of cold start and sparse data. In order to solve the deficiency of the collaborative filtering algorithm, we propose an improved hybrid method that combines the two kinds of information to generate word embedding and node embedding, avoiding the cold start problem and data sparsity problem. Experiments on real-world data sets show that our proposed approach is better than five state-of-the-art approaches, which verifies the effectiveness of our approach.
Even though the number of services is increasing, a single service can just complete simple tasks. In the face of complex tasks, we require composite multiple services to complete them. For the purpose of improving the efficiency of web service composition, we propose a service composition approach based on an improved fireworks algorithm (FWA++). First, we use the strategy of random selection to keep N − 1 individuals for the next generation, and the purpose is to speed up the convergence speed of the FWA++ and enhance the search ability. Second, we randomize the total number of sparks and maximum amplitude of sparks for each generation. In this way, the search ability and the ability to jump out of the local optimal solution are dynamically balanced throughout the execution of the algorithm. Our experimental results show that compared with other existing approaches, the approach proposed in this paper is more efficient and stable.
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