数据驱动的移动应用用户接受度建模与预测

应用市场（app market）已经成为互联网环境下软件应用开发和交付的一种主流模式.相对于传统模式，应用市场模式下，软件的交付周期更短，用户的反馈更快，最终用户和开发者之间的联系更加紧密和直接.为应对激烈的竞争和动态演变的用户需求，移动应用开发者必须以快速迭代的方式不断更新应用，修复错误缺陷，完善应用质量，提升用户体验.因此，如何正确和综合理解用户对软件的接受程度（简称用户接受度），是应用市场模式下软件开发需考量的重要因素.近年来兴起的软件解析学（software analytics）关注大数据分析技术在软件行业中的具体应用，对软件生命周期中大规模、多种类的相关数据进行挖掘和分析，被认为是帮助开发者提取有效信息、作出正确决策的有效途径.从软件解析学的角度，首先论证了为移动应用构建综合的用户接受度指标模型的必要性和可行性，并从用户评价数据、操作数据、交互行为数据这3个维度给出基本的用户接受度指标.在此基础上，使用大规模真实数据集，在目标用户群体预测、用户规模预测和更新效果预测等典型的用户接受度指标预测问题中，结合具体指标，提取移动应用生命周期不同阶段的重要特征，以协同过滤、回归融合、概率模型等方法验证用户接受度的可预测性，并讨论了预测结果与特征在移动应用开发过程中可能提供的指导.

Data-driven Modeling and Prediction of User Acceptance for Mobile Apps

With the popularity of mobile Internet and smart mobile devices in recent years, the app market mode has become one of the main modes of software release. In this mode, app developers have to update their apps rapidly to keep competitive. In comparison with traditional software, the connection between end users and developers of mobile apps is closer with quicker release of software and feedback of users. Understanding and improving user acceptance of mobile apps inevitably becomes one of the main goals for developers to improve their apps. Meanwhile, there is a wealth of data covering different stages of the software cycle of mobile apps in the app-market-centered ecosystem. From the view of software analytics, with techniques such as machine learning and data mining, valuable information could be extracted from data including operation logs, user behavior sequence, etc. to help developers make decisions. This article first demonstrates the necessity and feasibility of building a comprehensive model of user acceptance indicators for mobile apps from a data-driven perspective, and provides basic indicators from three dimensions of user evaluation, operation, and usage. Furthermore, with large-scale datasets, specific indicators are given in three user acceptance prediction tasks, and features from different stages of the software cycle of mobile apps are extracted. With collaborative filtering, regression models, and probability models, the predictability of user acceptance indicators is verified, and the insight of the prediction results in the mobile app development process is provided.