Mashup recommendation model for trigger action programming paradigm

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 mashup applications using TAP, and around 24 million mashups have been reused by IFTTT users. Up to this date, existent research has not focused on using mashups as a content for recommendations. In this work, we propose a model for mashup recommendation for Trigger Action Programming. For that purpose we propose rating strategies for unrated mashups based on the mashup community dynamics. Then, for the purpose of recommendation, we propose a series of implicit factor features unique to the TAP environment. Finally, we test our approach using various recommendation algorithms using the 200,000 recipes dataset from the IFTTT platform and compared its performance for content recommendation.