Skip to yearly menu bar Skip to main content

Workshop: Machine Learning Meets Econometrics (MLECON)

Boosting engagement in ed tech with personalized recommendations

Ayush Kanodia


Recommendation systems are the backbone of some of the most successful companies in the world. Their fundamental feature is that they exhibit increasing gains to scale: the bigger the platform the more precise and impactful are the recommendations. Understanding how large a system needs to be and how much users' data is necessary to leverage the gains from personalized recommendations is key to deciding when to launch such a system; yet, there is a shortage of empirical evidence to guide this decision. The most prominent applications of recommendation systems are associated with the entertainment sector (e.g. Netflix, Spotify, YouTube) or online retail (e.g. Amazon or eBay); it is unclear how effective these systems are in other contexts. This paper aims to fill these gaps by carrying out an RCT-based analysis of the introduction of personalized recommendations into an ed-tech platform.