基于集体学习的移动应用用户界面模块智能探索
Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning
摘要：
移动应用界面通常由一组用户界面模块组成。如何正确设计这些用户界面模块对于手机应用程序实现用户满意度至关重要，然而，除了依靠设计师的判断之外，很少有方法来确定用户界面模块的设计变量。通常，需要费力的后处理步骤来验证每个设计变量的关键变化。因此，可以测试的设计解决方案数量非常有限。这是耗时的，几乎不可能找出最佳的设计解决方案，因为有许多模块。为此，我们介绍FEELER，一个框架，快速和智能地探索设计解决方案的用户界面模块与集体机器学习的方法。FEELER可以帮助设计师定量测量不同设计方案的偏好得分，旨在方便设计师方便快捷地调整用户界面模块。我们在两个真实数据集上进行了广泛的实验评估，以证明其在中国最受欢迎的移动应用程序之一百度应用程序中用户界面模块设计的真实案例中的适用性。
英文摘要：
A mobile app interface usually consists of a set of user interface modules. How to properly design these user interface modules is vital to achieving user satisfaction for a mobile app. However, there are few methods to determine design variables for user interface modules except for relying on the judgment of designers. Usually, a laborious post-processing step is necessary to verify the key change of each design variable. Therefore, there is a only very limited amount of design solutions that can be tested. It is timeconsuming and almost impossible to figure out the best design solutions as there are many modules. To this end, we introduce FEELER, a framework to fast and intelligently explore design solutions of user interface modules with a collective machine learning approach. FEELER can help designers quantitatively measure the preference score of different design solutions, aiming to facilitate the designers to conveniently and quickly adjust user interface module. We conducted extensive experimental evaluations on two real-life datasets to demonstrate its applicability in real-life cases of user interface module design in the Baidu App, which is one of the most popular mobile apps in China.
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。
