Research On API Service Credible Recommendation Method Based On Multidimensional Data Fusion

With the development of Saa S and SOA technologies,a large number of Web services have emerged on the Internet.However,in a dynamically changing network environment,the service may be unavailable or invalid.Under such circumstances,how to help users discover alternative services and whether the discovered services are credible is a very challenging issue,which has attracted extensive attention from researchers at home and abroad.However,existing trusted recommendation methods often recommend based on the single information of the service,ignoring the fusion of multi-dimensional information of the service.Therefore,this article focuses on how to perform trusted service recommendation based on multidimensional data information of services.The main research contents are as follows:(1)This paper proposes a similar API service recommendation method based on description information and specifications(Specs)information.This method addresses the problem that existing recommendation methods generally recommend based on a single informational service.It considers API service description information and specification information.First,the service description clustering of API description information through the LDA theme model,and the similarity Calculate and judge the theme cluster to which the replacement API(Target API)belongs;then consider the API specification information,and further filter the API under the theme cluster according to the Jaccard coefficient;on this basis,calculate the description information between the replacement API and the filtered API description information And rank the similarity of the similarity,so as to recommend the API with high similarity as a candidate alternative service;finally,the experiment was conducted through the real data set on the Programmableweb website.Experimental results show that the above method can effectively and accurately recommend similar API services.(2)Propose a credible recommendation method for API services based on credibility and attention.This method is based on the recommendation of similar API services and for the problem of data sparseness in traditional collaborative filtering algorithms.First,the API-related Specs information is fused into the data normalization process to fill in the null values;then the scores are considered different,and the scores are considered.Perform the averaging process;then calculate the user's and Mashup service's score predictions for similar API services from the aspects of user credibility and attention,Mashup servicecredibility and attention,and further obtain similar API services by adjusting the weight calculation The comprehensive score prediction of the website recommends services with high comprehensive scores among similar API services to users.Finally,experiments were conducted on the real data set on the Programmableweb website.Experimental results show that the above service recommendation method comprehensively considers the multidimensional data information of the service,which effectively improves the accuracy and credibility of the service recommendation.