Mashup Recommendation for Trigger Action Programming

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.