On Mashup Service Recommendation Based on Multi Attributes Fusion Rating Algorithm
Zhao Guo-don · Journal of Southwest China Normal University · 2015
The traditional Mashup service recommendation is a retrieval method based on keywords,the use of social and functional properties for the recommended API service is too less to make a comprehensive evaluation of the recommended API applicability,so the Mashup service recommendation based on multi attributes fusion rating algorithm has been proposed to solve this problem.Firstly,the climbing tools have been used to collect ProgrammableWeb Mashup service information,and the suffix stripping algorithm been used to modify the Mashup service label with noun form as the research and analysis data sets.Secondly,the API model has been extended with the social and the functions attributes,then the multiple attribute similarity weighted fusion has been used to evaluate candidate API fitness,which would be the API service recommendation basis.The experimental results show that,the multiple attribute fusion rating Mashup service recommendation algorithm has higher accuracy and faster computing time,which is feasible and effective.