Web API Combination Recommendation Method Based On Generation Model

With the continuous growth of Web API,a large number of heterogeneous and complex APIs are gradually forming an ecosystem in the process of competition and cooperation.The economic form of business exchanges with the ability or competitiveness of the enterprise using API technology as a network service is also quietly formed.From the perspective of API ecosystem mining,especially for service composition needs,it is an important technology challenge to develop API recommendation technology and create mashup service to meet complex business need.To this end,this thesis proposes a Web API recommendation method based on the generation model.First,according to the principle of generating topic models,a new generation model Mashup Topic Model(MTM)is proposed for API combinational logic based on Author Topic model and Tag-LDA model.The model represents the user needs based on the classification label variables in the API set,generates implicit topics from them,and successively generates API variables and the word variables used for the textual description of Mashup from the hidden topics.MTM jointly model the Mashup description,Mashup tags and the combination of APIs.It not only describes Mashup requirements,but also recommends API and Mashup words for the same time.Secondly,based on the MTM model,the corresponding API recommendation algorithm is designed,and a diversification strategy is utilized to filter the candidate set of APIs to improve the recommendation effect.Finally,a recommendation experiment is carried out by using real data from API ecosystem website ProgrammableWeb.com.The experimental results demonstrate that the proposed API recommendation method is effective.In addition,a small application is designed to display the recommendation function.The main contribution of this thesis is that the proposed MTM realizes the comprehensive modeling of Mashup requirement,text description and combinational logic.It can also recommend API and description vocabulary for Mashup service need.This provides an effective solution to the technical challenges of API recommendation for Mashup.