Popularity of mobile apps is traditionally measured by metrics such as the number of downloads, installations, or user ratings. A problem with these measures is that they reflect usage only indirectly. Indeed, retention rates, i.e., the number of days users continue to interact with an installed app, have been suggested to predict successful app lifecycles. We conduct the first independent and large-scale study of retention rates and usage trends on a dataset of app-usage data from a community of 339,842 users and more than 213,667 apps. Our analysis shows that, on average, applications lose 65% of their users in the first week, while very popular applications (top 100) lose only 35%. It also reveals, however, that many applications have more complex usage behaviour patterns due to seasonality, marketing, or other factors. To capture such effects, we develop a novel app-usage trend measure which provides instantaneous information about the popularity of an application. Analysis of our data using this trend filter shows that roughly 40% of all apps never gain more than a handful of users ( Marginal apps). Less than 0.1% of the remaining 60% are constantly popular ( Dominant apps), 1% have a quick drain of usage after an initial steep rise ( Expired apps), and 6% continuously rise in popularity ( Hot apps). From these, we can distinguish, for instance, trendsetters from copycat apps. We conclude by demonstrating that usage behaviour trend information can be used to develop better mobile app recommendations.

中文翻译：

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利用使用情况来预测即时应用程序流行度

传统上，移动应用程序的受欢迎程度是通过下载次数、安装次数或用户评分等指标来衡量的。这些措施的一个问题是它们仅间接反映使用情况。事实上，保留率，即用户继续与已安装应用程序交互的天数，已被建议用于预测成功的应用程序生命周期。我们对来自 339,842 名用户和超过 213,667 个应用程序的社区的应用程序使用数据集进行了首次独立的大规模研究。我们的分析表明，平均而言，应用程序在第一周失去了 65% 的用户，而非常受欢迎的应用程序（前 100 名）仅损失了 35%。然而，它也表明，由于季节性、营销或其他因素，许多应用程序具有更复杂的使用行为模式。为了捕捉这样的效果，我们开发了一种新颖的应用程序使用趋势测量方法，可提供有关应用程序流行度的即时信息。使用此趋势过滤器分析我们的数据表明，大约 40% 的应用程序从未获得超过少数用户（边缘应用）。剩下的 60% 中只有不到 0.1% 持续流行（主导的应用程序），1% 的用户在最初的急剧上升后迅速耗尽（已到期应用程序），并且 6% 的受欢迎程度持续上升（热的应用）。例如，我们可以从中区分出潮流引领者和模仿应用程序。最后，我们证明使用行为趋势信息可用于开发更好的移动应用推荐。