A Bayesian learning model for design-phase service mashup popularity prediction
作者：
Highlights：
• First in-depth investigation on the popularity of mashups using a real-world dataset.
• Five factors were identified as key factors behind service mashup’s popularity.
• Propose a Bayesian model that offers valuable design-phase predictions and insights.
• Suggested approach can overcome data sparsity and capture popularity contribution.
• Conduct extensive experiments over a ProgrammableWeb dataset (5 years period).
摘要
•First in-depth investigation on the popularity of mashups using a real-world dataset.•Five factors were identified as key factors behind service mashup’s popularity.•Propose a Bayesian model that offers valuable design-phase predictions and insights.•Suggested approach can overcome data sparsity and capture popularity contribution.•Conduct extensive experiments over a ProgrammableWeb dataset (5 years period).
论文关键词：Popularity prediction,Bayesian learning,Service mashup
论文评审过程：Received 12 September 2018, Revised 6 January 2020, Accepted 21 January 2020, Available online 21 January 2020, Version of Record 3 February 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113231
