Investigating app store ranking algorithms using a simulation of mobile app ecosystems
Investigating app store ranking algorithms using a simulation of mobile app ecosystems
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使用移动应用生态系统模拟研究应用商店排名算法
DOI：
10.1109/cec.2013.6557892
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
P. Bentley
中科院分区：
文献类型：
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作者：
Soo Ling Lim;P. Bentley
应用商店是当今向移动设备用户提供内容的最流行方式之一。但是，每天有成千上万的竞争应用程序和成千上万个新的应用程序，向用户展示开发人员的应用程序的问题变得并非繁琐。所有内容都可能有一个应用程序，但是如果用户找不到所需的应用程序，那么App Store就会失败。本文使用AppeCo（移动应用程序生态系统的人造生活模型）调查了应用商店内容组织。在Appeco中，开发人员代理将应用程序构建和上传到App Store；用户代理浏览商店并下载应用程序。本文使用Appeco来研究如何最好地组织Apple iOS App Store中的顶级应用程序图表和新应用图表。我们研究了顶级应用程序图表的不同应用程序排名算法的影响以及新应用图表的更新频率在下载与浏览比率上。结果表明，商店界面的有效性在很大程度上取决于更新内容的速度。缓慢更新的新应用图表将影响顶级应用程序图表的有效性。通过包含过多的历史数据来衡量成功的顶级应用程序图也会对应用程序下载产生不利影响。
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 nontrivial. 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.