Mashup Recommendation for Trigger Action Programming
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.
KeywordsRecommendation systems Mashup recommendation Trigger-action programming
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).
- 1.Bianchini, D., De Antonellis, V., Melchiori, M.: WISeR: a multi-dimensional framework for searching and ranking web APIs. ACM Trans. Web (TWEB) 11(3), 19 (2017)Google Scholar
- 2.Elmeleegy, H., Ivan, A., Akkiraju, R., Goodwin, R.: Mashup advisor: a recommendation tool for mashup development. In: ICWS 2008. International Conference on Web Services. IEEE (2008)Google Scholar
- 3.George, T., Merugu, S.: A scalable collaborative filtering framework based on co-clustering. In: 5th International Conference on Data Mining. IEEE (2005)Google Scholar
- 6.Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2008)Google Scholar
- 9.Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning. ACM (2007)Google Scholar
- 10.Ur, B., McManus, E., Ho, M.P.Y., Littman, M.L.: Practical trigger-action programming in the smart home. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM (2014)Google Scholar
- 11.Ur, B., Ho, M.P.Y., Brawner, S., Lee, J., Mennicken, S., Picard, N., Schulze, D., Littman, M.L.: Trigger-action programming in the wild. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems - CHI 2016 (2016)Google Scholar