关键词: 流式应用, 情境感知, Xgboost, 机器学习, 个性化推荐

Abstract: In the current streaming mobile application distribution system, the distribution of applications relies manually install/uninstall either by mobile user or administrator, which does not take mobile users’ context information into account. To address these problems, it proposes a context-aware streaming mobile application recommendation mechanism. It collects the user’s context information data and uses the Xgboost algorithm to identify user’s context. Then, it recommends applications to user according to the identified context information. At the same time it uses the user’s feedback information to improve the accuracy of user-specific application recommendations. The experimental results show that the accuracy and time overhead of Xgboost algorithm is better than traditional algorithms, is practical in streaming application distribution system.

Key words: streaming application, context-aware, Xgboost, machine learning, personalized recommendation