The influence of explanations in recommender systems on user engagement

Abstract: Recommender systems are without a doubt a staple of the modern internet. Services like Amazon, Netflix, YouTube and Spotify rely on them. What makes them so engaging that millions of users spent billions of hours on them every day? User engagement is widely accept as a core concept of user experience but we still don’t know what role the user interface plays into it. This thesis investigates the effect of explanations in recommender systems on the users engagement with a case study on BMW Financial Services Thailand’s recommender system. An experiment on Amazon Mechanical Turks with the User Engagement Scale and A/B testing with Google Analytics proved a significant influence of explanations on the users engagement.