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Mashup Recommendation for Trigger Action Programming

  • Noé DomínguezEmail author
  • In-Young Ko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10845)

Abstract

If This Then That (IFTTT) is a popular platform that deploys mashed-up applications for end users using trigger-action programming (TAP) paradigm. To date, there are about 135 thousand mashup creators who have shared recipes for developing applications using TAP, and around 24 million mashups have been adopted by users. Up to this date, research has not focused on recommending personalized mashups for the users. In this work, we propose a model for mashup recommendation for Trigger Action Programming. We tested our recommendation algorithm using the 200,000 recipes dataset from the IFTTT platform and compared its performance with other popular algorithms for content recommendation.

Keywords

Recommendation systems Mashup recommendation Trigger-action programming 

Notes

Acknowledgments

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2016R1A2B4007585).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ComputingKorea Advanced Institute of Science and Technology (KAIST)DaejeonRepublic of Korea

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