Abstract
Due to the lack of domain and interface knowledge, it is difficult for users to create suitable service processes according to their needs. Thus, the paper puts forward a new service composition recommendation method. The method is composed of two steps: the first step is service component recommendation based on recurrent neural network (RNN). When a user selects a service component, the RNN algorithm is exploited to recommend other matched services to the user, aiding the completion of a service composition. The second step is service composition recommendation based on Naive Bayes. When the user completes a service composition, considering the diversity of user interests, the Bayesian classifier is used to model their interests, and other service compositions that satisfy the user interests are recommended to the user. Experiments show that the proposed method can accurately recommend relevant service components and service compositions to users.
Highlights
With the rapid development of Web 2.0, users gradually participate in the creation of web content
Previous researchers mainly utilized the topic model to obtain the latent topics for the improvement of the recommendation accuracy, for instance, the Latent Dirichlet Allocation (LDA) [5]
In the Mobile Internet and the 5G era, users, pay more attention to their functional requirements. erefore, this paper proposes a service composition recommendation method based on the recurrent neural network (RNN) and Naive Bayes. e RNN is used to ensure the relevance between word orders
Summary
With the rapid development of Web 2.0, users gradually participate in the creation of web content. E user-oriented lightweight service composition allows users to drag and drop service components on a lightweight service composition platform to generate a new service sequence. Users can create service compositions through a visual operation interface without programming skills Both the industry and the academia have shown great interest in this user-oriented lightweight service composition method. E method is divided into two stages: (1) When a user’s initial interests are unknown, according to the user’s selection, the method firstly recommends n service components with the highest correlation to the user by the RNN algorithm. Experiments show that the proposed method is able to accurately recommend service components and service compositions to users
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