用于预测移动应用程序安装的复合社交网络
Composite Social Network for Predicting Mobile Apps Installation
摘要：
我们通过给参与者提供运行我们传感软件的Android智能手机，对居住在大学研究生宿舍的大部分人口进行了仔细的测量。在本文中，我们提出了预测移动应用程序（称为“应用程序”）安装使用社交网络的新问题，并解释其挑战。现代智能手机，就像我们研究中使用的那些，能够使用内置传感器收集不同的社交网络。(e.g.虽然iPhone AppStore等应用程序市场制造商可以访问这些信息，但尚未研究应用程序市场制造商如何使用这些信息进行营销研究和战略制定。我们开发了一个简单的计算模型，通过使用从手机感知的不同网络计算出的复合网络来更好地预测应用程序安装。我们的模型还捕捉了应用程序采用中的个体差异和外部因素。我们展示了在预测应用程序安装时考虑所有这些因素的重要性，我们观察到令人惊讶的结果，即应用程序安装确实是可预测的。我们还表明，与通用方法相比，我们的模型获得了最佳结果：我们的结果比随机猜测好四倍，并且预测了近45%的用户安装的所有应用程序，精度接近45%（F1得分= 0.43）。
英文摘要：
We have carefully instrumented a large portion of the population living in a university graduate dormitory by giving participants Android smart phones running our sensing software. In this paper, we propose the novel problem of predicting mobile application (known as "apps") installation using social networks and explain its challenge. Modern smart phones, like the ones used in our study, are able to collect different social networks using built-in sensors. (e.g. Bluetooth proximity network, call log network, etc) While this information is accessible to app market makers such as the iPhone AppStore, it has not yet been studied how app market makers can use these information for marketing research and strategy development. We develop a simple computational model to better predict app installation by using a composite network computed from the different networks sensed by phones. Our model also captures individual variance and exogenous factors in app adoption. We show the importance of considering all these factors in predicting app installations, and we observe the surprising result that app installation is indeed predictable. We also show that our model achieves the best results compared with generic approaches: our results are four times better than random guess, and predict almost 45% of all apps users install with almost 45% precision (F1 score= 0.43).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
