Mashup Recommendation by Regularizing Matrix Factorization with API Co-Invocations

Lina Yao, Xianzhi Wang, Quan Z. Sheng, Boualem Benatallah, Chaoran Huang

Research output: Contribution to journalArticle

19 Citations (Scopus)


Mashup is a dominant approach for building data-centric applications, especially mobile applications, in recent years. Since mashups are predominantly based on public data sources and existing APIs, it requires no sophisticated programming knowledge of people to develop mashup applications. The recent prevalence of open APIs and open data sources in the Big Data era has provided new opportunities for mashup development, but at the same time increase the difficulty of selecting the right services for a given mashup task. The API recommendation for mashup differs from traditional service recommendation tasks in lacking the specific QoS information and formal semantic specification of the APIs, which limits the adoption of many existing methods. Although there are a significant number of service recommendation approaches, most of them focus on improving the recommendation accuracy and few work pays attention to the diversity of the recommendation results. Another challenge comes from the existence of both explicit and implicit correlations among the different APIs generally neglected by existing recommendation methods. In this paper, we address the above deficiencies of existing approaches by exploring API recommendation for mashups in the reusable composition context, with the goal of helping developers identify the most appropriate APIs for composition task

Original languageEnglish
JournalIEEE Transactions on Services Computing
Publication statusE-pub ahead of print - 6 Feb 2018


  • Correlation
  • Google
  • latent variable model
  • mashup
  • Mashups
  • Matrix decomposition
  • matrix factorization
  • Quality of service
  • Recommendation
  • Task analysis

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