Investigating App Store Ranking Algorithms using a Simulation of Mobile App Ecosystems

Abstract—App stores are one of the most popular ways of providing content to mobile device users today. But with thousands of competing apps and thousands new each day, the problem of presenting the developers ’ apps to users becomes non-trivial. There may be an app for everything, but if the user cannot find the app they desire, then the app store has failed. This paper investigates app store content organisation using AppEco, an Artificial Life model of mobile app ecosystems. In AppEco, developer agents build and upload apps to the app store; user agents browse the store and download the apps. This paper uses AppEco to investigate how best to organise the Top Apps Chart and New Apps Chart in Apple’s iOS App Store. We study the effects of different app ranking algorithms for the Top Apps Chart and the frequency of updates of the New Apps Chart on the download-to-browse ratio. Results show that the effectiveness of the shop front is highly dependent on the speed at which content is updated. A slowly updated New Apps Chart will impact the effectiveness of the Top Apps Chart. A Top Apps Chart that measures success by including too much historical data will also detrimentally affect app downloads. Keywords-mobile app ecosystems; Artificial Life; agent-based simulation; app store; top apps chart; new apps chart; evolving developer strategies I